A method for real-time compensation of attitude pointing error based on hybrid nonlinear model

Abstract

The application discloses a kind of real-time compensation methods of attitude pointing error based on mixed nonlinear model, belong to space laser communication and optical machine control technical field.The method includes: the mechanism of action of three-axis laser gyroscope error source (zero offset, scale factor and shaft system non-orthogonal, etc.) to measurement is analyzed;Establish the mixed nonlinear error model between real attitude and measured attitude;Based on error transmission and coordinate system transformation theory, convert attitude error into rotation matrix and embed geometric pointing error link, build the synchronous compensation model of attitude and geometric coupling error;Fusion multi-source observation data, using recursive least squares and robust estimation combined online identification algorithm, realize the real-time dynamic estimation of model parameters.The application has model explainability and computational efficiency, can effectively compensate the pointing error under complex attitude condition, improve the dynamic pointing accuracy and link stability of motion platform optical communication terminal.

Description

Technical Field

[0001] This invention belongs to the field of space laser communication and optomechanical control technology, specifically relating to a real-time attitude pointing error compensation method based on a hybrid nonlinear model. Background Technology

[0002] Space laser communication boasts advantages such as high bandwidth, high security, and strong anti-interference capabilities, making it a key technology for constructing an integrated space-air-ground information network. Compared to fixed platforms, optical communication terminals mounted on moving platforms (such as satellites, aircraft, and vehicle-mounted terminals) offer advantages in flexible deployment and high mobility, supporting various link configurations including space-air, air-to-air, and ground-based mobile communication, significantly expanding the application scenarios of laser communication.

[0003] In laser communication systems, pointing accuracy is a core factor determining the success rate of link establishment and maintaining stability. The dynamic operation of a moving platform continuously changes its posture, causing the pointing reference to drift, placing stringent requirements on optical axis pointing. Achieving high-precision pointing presupposes accurate and real-time acquisition of platform attitude information. Currently, platform attitude measurement mainly relies on a combined navigation scheme of Inertial Navigation System (INS) and Global Positioning System (GPS). Among these, INS, based on an Inertial Measurement Unit (IMU), autonomously calculates attitude, velocity, and position using gyroscopes and accelerometers. It boasts advantages such as high output frequency, fast response, and independence from external signals, making it the core attitude sensing device for moving platforms.

[0004] However, INS, especially its core component gyroscope, inherently suffers from systematic errors and random noise when outputting attitude information. These errors primarily stem from factors such as gyroscope zero-bias instability, nonlinear scaling factors, non-orthogonality between axes, and random walk noise. These errors accumulate during integration, leading to significant deviations in the output attitude angles (heading, pitch, roll). For long-distance, narrow-beam laser communication, even minute attitude angle errors, amplified by the optomechanical system, can cause severe pointing deviations, and such errors are difficult to correct directly using external means like GPS.

[0005] Existing pointing error compensation technologies mostly focus on static or quasi-static correction of geometric errors such as installation deviations and mechanical deformations, while lacking systematic modeling and real-time compensation methods for dynamic attitude errors introduced by the INS (Instrument System). In fact, under dynamic operating conditions, attitude errors and geometric errors are coupled and work together in the pointing link, forming complex compound pointing deviations. Traditional separate correction methods are difficult to cope with this coupling effect, especially when the platform undergoes large maneuvers or complex attitude changes, resulting in a significant decrease in pointing accuracy.

[0006] Therefore, it is urgent to conduct in-depth research on the dynamic propagation mechanism of INS attitude error, establish an error model that can uniformly describe the coupling effect between attitude and geometry, and develop a set of efficient algorithms that can estimate online and compensate in real time, so as to improve the pointing accuracy and system robustness of the motion platform optical communication terminal in complex dynamic environments. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a real-time attitude pointing error compensation method based on a hybrid nonlinear model, which combines model interpretability with computational efficiency. This method can effectively compensate for pointing errors under complex attitude conditions, thereby improving the dynamic pointing accuracy and link stability of the optical communication terminal on the motion platform.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A real-time attitude pointing error compensation method based on a hybrid nonlinear model includes:

[0010] Step 1: Analyze the influence mechanism of the zero bias, scale factor error and axis non-orthogonality error of the three-axis laser gyroscope on the measured angular velocity;

[0011] Step 2: Based on the analysis results of the influence mechanism, use a hybrid nonlinear model to establish the error function relationship between the platform's actual attitude and the inertial navigation measurement attitude, and obtain the attitude error;

[0012] Step 3: Based on the theory of error propagation and coordinate system transformation, the attitude error is converted into a rotation correction matrix under small angle conditions, and the rotation correction matrix is ​​embedded into the existing geometric pointing error propagation link to construct a coupled parameterized model that can synchronously compensate for attitude error and geometric error.

[0013] Step 4: Integrate multi-source observation data and use an online identification algorithm that combines recursive least squares update mechanism and robust weighted estimation to estimate and update the parameters in the coupled parameterized model in real time, thereby achieving dynamic compensation for attitude pointing error.

[0014] In a second aspect, the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned real-time attitude pointing error compensation method based on a hybrid nonlinear model.

[0015] Thirdly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned real-time attitude pointing error compensation method based on a hybrid nonlinear model.

[0016] The beneficial effects of this invention are as follows:

[0017] The mechanism modeling is clear and the error source is accurately traced: Through in-depth analysis of error sources such as zero bias, scaling factor and non-orthogonality of the axis system of the three-axis laser gyroscope, an analytical model from angular velocity measurement error to attitude angle deviation is established, revealing the formation and accumulation mechanism of dynamic attitude error, and laying a theoretical foundation for accurate compensation.

[0018] Unified coupled modeling and efficient compensation: The attitude error is innovatively embedded into the geometric pointing error transmission link in the form of a rotation matrix, and a coupled parameter model that can synchronously correct attitude and geometric errors is constructed. This solves the problem of incomplete compensation caused by the separate processing of traditional methods and improves the overall correction efficiency under complex attitudes.

[0019] Robust online identification and real-time dynamic correction: An adaptive algorithm combining recursive least squares and robust estimation is employed to estimate model parameters online using multi-source observation data. This algorithm adapts to time-varying parameters through a forgetting factor and suppresses observational abrupt changes and noise interference through a weighting mechanism, ensuring the robustness of the identification process and achieving real-time, dynamic error compensation without system interruption.

[0020] Balancing interpretability and practicality: The established hybrid nonlinear model retains the physical meaning of key error terms (such as affine transformations and constant biases) while characterizing complex nonlinear relationships through polynomial terms, thus balancing the model's interpretability with its ability to fit complex errors. The overall method is computationally efficient, easy to implement in engineering, and significantly improves the dynamic pointing accuracy, link stability, and real-time performance of optical communication terminals on motion platforms. Attached Figure Description

[0021] Figure 1 This is a flowchart of a real-time attitude pointing error compensation method based on a hybrid nonlinear model according to the present invention.

[0022] Figure 2 A schematic diagram of the installation of a three-axis laser gyroscope;

[0023] Figure 3 The diagram shows the errors of the gyroscope, where (a) is a diagram of the bias and scaling factor errors; and (b) is a diagram of the non-orthogonal error of the axis system. Detailed Implementation

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] like Figure 1 As shown, this invention provides a real-time attitude pointing error compensation method based on a hybrid nonlinear model, comprising:

[0026] Step 1: Analyze the influence mechanism of the zero bias, scale factor error and axis non-orthogonality error of the three-axis laser gyroscope on the measured angular velocity;

[0027] Step 2: Based on the analysis results of the influence mechanism, a hybrid nonlinear model containing affine transformation terms, constant bias terms, and polynomial nonlinear terms is used to establish the error function relationship between the platform's true attitude and the inertial navigation measurement attitude, and to obtain the attitude error.

[0028] Step 3: Based on the theory of error propagation and coordinate system transformation, the attitude error is converted into a rotation correction matrix under small angle conditions, and the rotation correction matrix is ​​embedded into the existing geometric pointing error propagation link to construct a coupled parameterized model that can synchronously compensate for attitude error and geometric error.

[0029] Step 4: Integrate multi-source observation data from inertial navigation, image sensor, encoder, and the relative position of the platform and the target, and use an online identification algorithm that combines recursive least squares update mechanism and robust weighted estimation to estimate and update the parameters in the coupled parameterized model in real time, so as to realize dynamic compensation for attitude pointing error.

[0030] Specifically, in step 1, such as Figure 2 As shown, the three-axis laser gyroscope consists of three laser gyroscope units mounted in mutually orthogonal planes. Each gyroscope measures angular velocity along the three orthogonal axes of the platform based on the Sagnac effect, achieving independent measurement of three-dimensional angular velocity. Figure 3 of (a) Figure 3 As shown in (b), due to design, manufacturing, and assembly errors, the output of a gyroscope contains various error factors, such as zero bias, scaling factor error, non-orthogonality error, and random noise. Among them: zero bias causes attitude estimation error to accumulate over time; scaling factor error causes measurement results to be amplified or reduced; non-orthogonality error causes inter-axis coupling; and random noise, after integration, forms random walk error.

[0031] Therefore, the mechanism by which various error factors of the gyroscope affect the angular velocity measurement results is as follows:

[0032]

[0033] in, This represents the true value of the platform's angular velocity. Here, I represents the angular velocity measurement from the gyroscope, I is the identity matrix, and K is the scale factor error matrix. This is the non-orthogonal error matrix between the axis systems. It is a zero-bias vector. The random noise present in the system is expressed by the following expressions:

[0034]

[0035]

[0036] in, , , These represent the zero bias coefficients of the heading, pitch, and roll gyroscopes, respectively. , , These represent the random noise of the heading, pitch, and roll gyroscopes, respectively. , , These represent the scaling factor errors of the heading, pitch, and roll gyroscopes, respectively. , , , , , These represent the non-orthogonal error coefficients between the corresponding axis systems. The angular velocity measured by the gyroscope can be obtained from the above relationships. Compared with true angular velocity The function mapping between them provides a foundation for attitude error modeling.

[0037] In step 2, assuming that the error parameter remains constant over a short period of time, the two sides of the above angular velocity equation are integrated to obtain the relationship between the attitude measurement value and the true value:

[0038]

[0039] in, For the true pose value, The attitude measurement values ​​are obtained from inertial navigation systems, where , , These are the actual values ​​of the heading angle, azimuth angle, and roll angle, respectively. , , These are the inertial navigation measurements of heading angle, azimuth angle, and roll angle, respectively, where T represents the measurement duration. This represents noise. It is evident that the attitude measurement error accumulates over time and manifests as a nonlinear function. To preserve the interpretability of the parameters, only the static and polynomial-approximable dynamic parts are retained, simplifying the model in equation (4) to:

[0040]

[0041] The attitude measurement error function can be written as:

[0042]

[0043] in, , , , These represent the measurement errors of the heading angle, azimuth angle, and roll angle, respectively. This is the affine transformation matrix, used to represent the scaling error and the non-orthogonal error. A constant bias vector, These are second-order polynomial functions used to characterize the nonlinear relationships between attitudes. Their respective expressions are:

[0044]

[0045] in, , , Used for equivalent proportional error , , , , , Used for non-orthogonal errors, , , , , , , , , , , , , , , , , , The coefficients are second-order polynomials. Formula (6) has a strong error fitting ability and can simultaneously describe constant deviation, proportional error and higher-order nonlinear terms.

[0046] In step 3, when the attitude measurement error is a small angle error, a corresponding rotation correction matrix can be established:

[0047]

[0048] in, , , These represent the error rotation matrices for heading, pitch, and roll, respectively.

[0049] The comprehensive attitude error correction matrix is ​​defined based on the rotation sequence of heading, pitch, and roll. ,have:

[0050]

[0051] When unfolded under small angle conditions, we get:

[0052]

[0053] Using the theory of error propagation and coordinate system transformation, formula (10) can be introduced into the pointing error link to obtain a pointing error correction model containing attitude error terms:

[0054]

[0055]

[0056]

[0057]

[0058] in, and The encoder guidance value is the guidance value calculated by the motion optical communication terminal when it is pointing at the target to be observed, based on the positional relationship between the target to be observed and the motion optical communication terminal in space, combined with the inertial navigation measurement value of the motion optical communication terminal. and The pointing error is the difference between the encoder guidance value and the encoder reading when the motion optical communication terminal is pointing at the target to be observed. This is an existing information matrix related to geometric errors; This is a parameter vector related to geometric errors.

[0059] In step 4, the multi-source observation data includes observation data from the inertial navigation system, image sensor, encoder, and the relative position information between the platform and the target. The recursive least squares algorithm is as follows:

[0060]

[0061]

[0062]

[0063] in, Let be the observation vector at time k; For information matrix, by and Composed by splicing columns; Let be the vector of parameters to be estimated, from and Composed by stitching together rows; The parameter is the covariance matrix; This is the gain matrix; Forgetting factor is used to reduce the weight of historical observations so that the estimation can adapt to the time-varying characteristics of the parameters. Substituting formulas (11)-(14) into formulas (15)-(17) can realize the online identification of parameters. After obtaining the identification results, formula (11) can be used to calculate the corresponding prediction pointing error, and finally realize the pointing error compensation.

[0064] The above method was tested on airborne and vehicle-mounted platforms, and the experimental results are shown in Tables 1 and 2:

[0065] Table 1

[0066]

[0067] Table 2

[0068]

[0069] As shown in Tables 1 and 2, the experimental results of airborne optical communication terminals and vehicle-mounted optical communication terminals consistently demonstrate that, compared to other models, the model proposed in this paper can significantly improve the robustness, error stability, and pointing accuracy of the system by compensating for the static attitude error of the inertial navigation system.

[0070] In a second aspect, the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned real-time attitude pointing error compensation method based on a hybrid nonlinear model.

[0071] Thirdly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned real-time attitude pointing error compensation method based on a hybrid nonlinear model.

[0072] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A real-time attitude pointing error compensation method based on a hybrid nonlinear model, characterized in that, include: Step 1: Analyze the influence mechanism of the zero bias, scale factor error and axis non-orthogonality error of the three-axis laser gyroscope on the measured angular velocity; Step 2: Based on the analysis results of the influence mechanism, use a hybrid nonlinear model to establish the error function relationship between the platform's actual attitude and the inertial navigation measurement attitude, and obtain the attitude error; Step 3: Based on the theory of error propagation and coordinate system transformation, the attitude error is converted into a rotation correction matrix under small angle conditions, and the rotation correction matrix is ​​embedded into the existing geometric pointing error propagation link to construct a coupled parameterized model that can synchronously compensate for attitude error and geometric error. Step 4: Integrate multi-source observation data and use an online identification algorithm that combines recursive least squares update mechanism and robust weighted estimation to estimate and update the parameters in the coupled parameterized model in real time, thereby achieving dynamic compensation for attitude pointing error.

2. The real-time attitude pointing error compensation method based on a hybrid nonlinear model according to claim 1, characterized in that, In step 1, a mapping model is established between the angular velocity measured by the gyroscope and the actual angular velocity of the platform. The actual angular velocity is obtained by linearly transforming the measured angular velocity through a combination matrix consisting of an identity matrix, a scaling factor error matrix, and an axis non-orthogonal error matrix, and then adding a zero bias vector and a random noise vector.

3. The real-time attitude pointing error compensation method based on a hybrid nonlinear model according to claim 1, characterized in that, In step 2, the attitude error is the difference between the platform's true attitude vector and the inertial navigation measurement attitude vector; the hybrid nonlinear model includes affine transformation terms, constant bias terms, and polynomial nonlinear terms.

4. The real-time attitude pointing error compensation method based on a hybrid nonlinear model according to claim 1, characterized in that, In step 3, the rotation correction matrix is ​​a 3×3 matrix, whose matrix elements are linearly combined from the heading, pitch, and roll error components in the attitude measurement error vector and the trigonometric function values ​​of the heading, pitch, and roll angles calculated from the attitude measurement vector.

5. The real-time attitude pointing error compensation method based on a hybrid nonlinear model according to claim 1, characterized in that, In step 3, the process of constructing the coupled parameterized model includes: using coordinate system transformation theory, cascading or fusing the rotation correction matrix describing the attitude error with the transformation matrix describing the geometric pointing error to form a unified, parameterized total error correction transformation.

6. The real-time attitude pointing error compensation method based on a hybrid nonlinear model according to claim 1, characterized in that, In step 4, the recursive least squares update mechanism introduces a forgetting factor. By iteratively calculating the gain matrix, updating the parameter estimates, and updating the parameter covariance matrix, adaptive online estimation of model parameters is achieved. The forgetting factor is used to reduce the weight of old observation data to adapt to time-varying parameters.

7. The real-time attitude pointing error compensation method based on a hybrid nonlinear model according to claim 1, characterized in that, In step 4, the robust weighted estimation assigns weights to each observation based on the predicted residuals of each observation using a weighting function. In the objective function of parameter update, residuals with small weights are given a lower impact. At the same time, a regularization penalty term for parameter changes is introduced to enhance the algorithm's robustness against gross errors and abrupt changes in the observation data.

8. The real-time attitude pointing error compensation method based on a hybrid nonlinear model according to claim 1, characterized in that, The multi-source observation data fused in step 4 includes observation data from the inertial navigation system, image sensor, encoder, and the relative position information of the platform and the target.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the attitude pointing error real-time compensation method based on a hybrid nonlinear model as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, enable the processor to implement the attitude pointing error real-time compensation method based on a hybrid nonlinear model as described in any one of claims 1-8.

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