Polarization / pressure sensor parameter online mutual calibration method

Abstract

The application discloses a kind of polarization / sun-sensing sensor parameter online mutual calibration methods, belong to bionic polarization combined navigation technical field.The method core is, respectively using the polarization vector measured by polarization sensor and the sun vector measured by sun-sensing, constructs the cross-measurement equation for calibrating the parameter of the other party, realizes the mutual correction of the parameter of two sensors.To cope with complex environment, the application innovatively based on the Euclidean distance of polarization degree and the sun image coordinate and sun-sensing main point coordinate, designs the reliability discrimination function of polarization measurement and sun-sensing measurement, and accordingly formulates adaptive online mutual calibration switching strategy.Finally, the method of unscented Kalman filter is used to carry out online joint estimation to state quantity.The application can realize the parameter online mutual calibration of polarization sensor and sun-sensing without relying on high-precision turntable and other external reference, significantly improves the overall perception accuracy and long-term adaptability of sensor in time-varying environment.

Description

Technical Field

[0001] This invention belongs to the field of biomimetic polarization integrated navigation technology, specifically relating to an online mutual calibration method for polarization / polarity sensor parameters. Background Technology

[0002] Polarized light navigation and solar light navigation are two important autonomous navigation methods inspired by organisms in nature. Polarization sensors detect the polarization pattern of the sky to obtain the polarization vector, while solar sensors detect the solar field to obtain the solar vector; both can be used to interpret the vehicle's heading. Research shows that organisms (such as desert locusts) possess neural mechanisms for cross-correcting polarization and solar information, providing important insights for the design of polarization / solar-sensitive integrated navigation systems. A prerequisite for achieving high-performance polarization / solar-sensitive integrated navigation is that both sensors have high-precision perception models, the core of which lies in the accurate calibration of the internal parameters of the sensors.

[0003] Traditional polarization sensor and terahertz calibration methods heavily rely on offline calibration environments consisting of high-precision turntables and integrating spheres or star simulators. These methods only allow for a one-time parameter fixation before shipment, constituting offline calibration. However, in practical applications, sensor parameters drift due to various time-varying factors such as temperature changes, device aging, mechanical stress, and complex atmospheric conditions. This renders the fixed offline parameters inapplicable, leading to a sharp decline in navigation accuracy.

[0004] To improve environmental adaptability, online calibration technology has become a research hotspot. Existing technologies, such as "an online calibration method for polarization sensor errors based on a sun sensor," utilize the high-precision solar vector provided by the sun sensor to achieve online estimation of polarization sensor parameters. However, such methods implicitly rely on the crucial premise that the parameters of the sun sensor itself, serving as the reference, are accurate and unchanging. In reality, the sun sensor's internal parameters, such as focal length and principal point coordinates, also change with the environment and usage conditions. In long-endurance, cross-regional missions, if only the polarization sensor is calibrated online, but the sun sensor parameters contain errors, the calibration will fail, ultimately affecting the fusion accuracy of the entire integrated system. Currently, how to simulate biological mutual correction mechanisms to achieve online mutual calibration of parameters between the polarization sensor and the sun sensor without relying on an external high-precision turntable, in order to collaboratively cope with complex time-varying environments, remains a pressing technical challenge that needs to be overcome. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides an online mutual calibration method for polarization / taisen sensor parameters. This method fully utilizes the information from both the polarization sensor and the taisen sensor, enabling online mutual calibration of their parameters without relying on a high-precision turntable. This improves the model accuracy and environmental adaptability of the polarization sensor and the taisen sensor in complex scenarios.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for online mutual calibration of polarization / polarity sensor parameters, comprising:

[0008] Step 1: Using the polarization sensor's light intensity gain coefficient, polarization degree coefficient, polarizer installation angle, polarizer focal length, polarizer lateral principal point coordinates, and polarizer longitudinal principal point coordinates as system state variables, establish the polarization / polarizer online calibration state equation.

[0009] Step 2: Using the polarization vector measured by the polarization sensor, establish an online calibration and measurement equation for the polarization sensor parameters based on polarization; using the solar vector measured by the polarization sensor, establish an online calibration and measurement equation for the polarization sensor parameters based on the polarization sensor.

[0010] Step 3: Based on the Euclidean distance between the solar image coordinates and the principal point coordinates of the solar sensor, and the Euclidean distance between the solar image coordinates and the principal point coordinates of the solar sensor, establish a confidence discrimination function for polarization measurement and solar sensor measurement, and design an online mutual calibration switching strategy for polarization / solar sensor parameters.

[0011] Step 4: Based on the state equation established in Step 1, the measurement equation established in Step 2, and the switching strategy established in Step 3, use the unscented Kalman filter method to jointly estimate the system state variables and calibrate the polarization / thermosensitive sensor parameters online.

[0012] Furthermore, in step 1, the state variables for the online polarization / polarimeter calibration are selected as: polarization sensor light intensity gain coefficient, polarization degree coefficient, polarizer installation angle, polarimeter focal length, polarimeter lateral principal point coordinates, and polarimeter longitudinal principal point coordinates. The state equation is set such that the first derivative of the system state variables is zero, and system process noise is introduced to characterize the slow variation characteristics of the parameters.

[0013] Furthermore, in step 2, establishing the online calibration measurement equation for the polarization-based solar sensor parameters includes: calculating the solar vector angle and synthesizing the solar vector based on the solar image coordinates measured by the solar sensor, the focal length of the solar sensor to be calibrated, and the principal point coordinates; orthogonally constraining the calculated solar vector with the polarization vector and attitude transformation matrix measured by the polarization sensor, and constructing a first-class measurement equation with the mathematical relationship of the orthogonal constraint as the core.

[0014] Furthermore, in step 2, establishing the online calibration measurement equation for the polarization sensor parameters based on the solar sensor includes: calculating the reference polarization vector in the carrier coordinate system based on the solar vector measured by the solar sensor and the observation vector of the polarization sensor; substituting the polarization azimuth angle corresponding to the reference polarization vector into the physical model of the output light intensity of the polarization sensor, and constructing a second type of measurement equation with the output light intensity of each channel of the sensor as the core.

[0015] Furthermore, in step 3, the confidence discrimination function and the online mutual calibration switching strategy specifically involve: calculating the Euclidean distance between the solar image coordinates and the coordinates of the solar sensor principal point; setting a polarization degree threshold and a distance threshold; when the polarization degree is greater than or equal to the polarization degree threshold, selecting the polarization-based measurement equation to calibrate the solar sensor parameters; when the Euclidean distance is less than the distance threshold, selecting the solar sensor-based measurement equation to calibrate the polarization sensor parameters.

[0016] Furthermore, the polarization degree threshold is set based on the physical characteristic that the measurement accuracy of the polarization sensor increases with the increase of polarization degree.

[0017] Furthermore, the distance threshold is set based on the imaging characteristic that the accuracy of the solar sensor measurement decreases as the solar image point deviates from the center of the field of view.

[0018] Furthermore, in step 4, the process of joint estimation using the unscented Kalman filter method is as follows:

[0019] According to the switching strategy, at each moment, the currently effective measurement equation is selected, which together with the state equation constitutes the state estimation system; through the time update and measurement update steps of the unscented Kalman filter, the optimal estimate of the system state variables is obtained recursively, and the parameter calibration is completed.

[0020] 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 method for online mutual calibration of polarization / polarity sensor parameters.

[0021] 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 method for online mutual calibration of polarization / polarity sensor parameters.

[0022] The beneficial effects of this invention are as follows:

[0023] The invention achieves system-level online mutual calibration: It breaks through the limitations of unidirectional calibration and reliance on high-precision turntables in the prior art, and proposes for the first time a bidirectional online mutual calibration framework between polarization sensors and polarization sensors, realizing online joint estimation and compensation of the key internal parameters of the two at the system level.

[0024] Possessing high environmental adaptability and intelligent decision-making capabilities: This invention innovatively introduces a reliability discrimination function based on polarization degree and solar image coordinates, along with an adaptive switching strategy. This strategy can intelligently determine and select more reliable measurement information for calibration based on the physical characteristics of the current environment (such as the strength of sky polarization degree and the position of the sun in the field of view). This maintains the robustness and high accuracy of the calibration process under complex and variable weather and lighting conditions, greatly enhancing the system's ability to operate in time-varying environments.

[0025] This invention enhances the long-term reliability and ease of use of integrated navigation systems: By eliminating reliance on large, fixed calibration equipment such as high-precision turntables, the sensor systems can be mutually calibrated after deployment, effectively suppressing time-varying parameter drift and ensuring navigation accuracy for long-endurance missions. This not only reduces system maintenance costs and deployment complexity but also provides crucial technical support for achieving fully autonomous and highly reliable navigation in fields such as unmanned systems and aerospace. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the online mutual calibration method for polarization / high sensitivity sensor parameters according to the present invention;

[0027] Figure 2 The figure shows the simulation results of online mutual calibration of polarization-based thermocouple parameters according to the present invention.

[0028] Figure 3 The figure shows the simulation results of online mutual calibration of the polarization sensor parameters based on the present invention. Detailed Implementation

[0029] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0030] This invention provides an online mutual calibration method for polarization / thermal sensor parameters. First, the polarization sensor's light intensity gain coefficient, polarization degree coefficient, polarizer installation angle, thermal sensor focal length, and thermal sensor principal point coordinates are used as state variables to establish an online mutual calibration state equation. Second, using the thermal sensor's solar vector and the polarization sensor's polarization vector as measurements, online calibration measurement equations for the polarization sensor parameters and thermal sensor parameters are established respectively. Then, based on the degree of polarization, the Euclidean distance between the solar image coordinates and the thermal sensor principal point coordinates, a confidence discriminant function for polarization and thermal sensor measurements is established, and an online mutual calibration switching strategy is designed. Finally, an unscented Kalman filter method is used to estimate the state variables.

[0031] like Figure 1 As shown, the online mutual calibration method for polarization / polarity sensor parameters of the present invention includes the following steps:

[0032] Step 1: Using the polarization sensor's light intensity gain coefficient, polarization degree coefficient, polarizer installation angle, polarizer focal length, polarizer lateral principal point coordinates, and polarizer longitudinal principal point coordinates as system state variables, establish the polarization polarizer online calibration state equation. The specific process is as follows:

[0033] First, the system state variables are represented as matrix X:

[0034] (1)

[0035] in, Indicates the first The light intensity gain coefficient of the channel, Indicates the first The polarization coefficient of the channel. Indicates the first The polarizer mounting angle of the channel, , Indicates the total number of polarization sensor channels; Indicates the focal length of the sensitive sensor. This indicates the coordinates of the horizontal principal point of Taimin. The coordinates of the vertical principal point of the matrix are represented by T, and the superscript T indicates the transpose of the matrix.

[0036] Both the polarization sensor parameters and the lattice sensitivity parameters are slowly varying state variables, and their dynamic characteristics are difficult to characterize. Therefore, to simplify the state equations, the derivatives of the state variables are assumed to be zero, i.e.:

[0037] (2)

[0038] in, The system noise is represented by Gaussian white noise, i.e. variance matrix The polarization sensor and the high-sensitivity device are selected based on their horizontal orientation.

[0039] Step 2: Using the polarization vector measured by the polarization sensor, establish online calibration and measurement equations for the polarization sensor parameters based on polarization; using the solar vector measured by the polarization sensor, establish online calibration and measurement equations for the polarization sensor parameters based on the polarization sensor. The specific process is as follows:

[0040] When calibrating the solar sensitivity parameters using a polarization sensor, the solar sensitivity measures the solar image coordinates in the image coordinate system. The solar vector angle can be calculated from the coordinates of the solar image. and solar vector angle :

[0041] (3)

[0042] in, For the sensitive focal length, Define an intermediate function for the principal point coordinates of Taimin:

[0043] (4)

[0044] Solar vector measured by Taimin It can be represented as:

[0045] (5)

[0046] Polarization sensor 1 and polarization sensor 2 directly measure the polarization vector in the module coordinate system. and The corresponding polarization vector in the carrier coordinate system and It can be represented as:

[0047] (6)

[0048] in, and These represent the attitude transformation matrices between the two module coordinate systems and the carrier coordinate system, respectively.

[0049] When calibrating a calibrator using a polarization sensor, the polarization vector measured by the polarization sensor... and It is considered accurate, and an online calibration and measurement equation for the polarization-based thermocouple parameter can be established:

[0050] (7)

[0051] in, The measurement noise for polarization-based calibration of the thermosensitive parameters is all Gaussian white noise, i.e. variance matrix The polarization sensor and the high-sensitivity device are selected based on their horizontal orientation. and Represent matrices respectively and The One element;

[0052] When using a calibrated polarization sensor, the solar vector measured by the calibrator... It is considered accurate; taking polarization sensor 1 as an example, the polarization vector in the carrier coordinate system can be obtained. for:

[0053] (8)

[0054] in, The L2 norm is used to represent the polarization vector, which has positive and negative ambiguity, so the adjustment factor is... , It is the polarization angle. This represents the observation vector in the module coordinate system. To simplify the expression, auxiliary variables are constructed. and Mathematically, this is expressed as:

[0055] (9)

[0056] Based on the polarization sensor model, an online calibration and measurement equation for the polarization sensor parameters based on the polarization sensor can be established:

[0057] (10)

[0058] in, It is the input light intensity. It refers to the degree of polarization. The noise in the online calibration measurement of parameters of the polarization sensor based on Taimin is all Gaussian white noise, i.e. variance matrix The polarization sensor and the high-sensitivity device are selected based on their horizontal orientation.

[0059] Step 3: Based on the Euclidean distance between the solar image coordinates and the principal point coordinates of the solar sensor, and the Euclidean distance between the solar image coordinates and the principal point coordinates of the solar sensor, establish a confidence discrimination function for polarization measurement and solar sensor measurement, and design an online mutual calibration switching strategy for polarization and solar sensor parameters. The specific process is as follows:

[0060] Euclidean distance between solar image coordinates and Taimin principal point coordinates for:

[0061] (11)

[0062] Set polarization threshold and distance threshold According to the degree of polarization Euclidean distance between the solar image coordinates and the principal point coordinates of the solar sensor A reliability discrimination function for polarization measurement and susceptibility measurement is established, and an online mutual calibration switching strategy for polarization / susceptibility parameters is designed as follows:

[0063] (12)

[0064] when When polarization measurement is selected Calibrate the sensitivity parameters; when At that time, a sensitive measurement was selected. Calibrate the polarization sensor parameters.

[0065] Step 4: In step 4, combining steps 1, 2, and 3, based on the system state equation, measurement equation, and switching strategy, the unscented Kalman filter method is used to jointly estimate the system state variables, namely the polarization sensor light intensity gain coefficient, polarization degree coefficient, polarizer installation angle, polarizer focal length, and polarizer principal point coordinates, and to calibrate the polarization polarizer sensor parameters online.

[0066] Numerical simulations were performed to verify the method proposed in this invention. The specific simulation conditions for mutual calibration of polarization / sensitivity parameters are shown in Table 1 below:

[0067] Table 1

[0068]

[0069] , and These represent speeds in the east, north, and sky directions, respectively. , and These represent pitch, roll, and yaw angles, respectively.

[0070] Figure 2 The figure shows the simulation results of the online calibration of the polarization-based solar sensor parameters. As can be seen from the figure, the coordinates of the horizontal and vertical principal points of the solar sensor converge in about 200s, and the focal length converges in about 180s. Table 2 shows the online calibration results of the polarization-based solar sensor parameters. As can be seen from the table, the calibration errors of the horizontal principal point coordinates, the vertical principal point coordinates, and the focal length are 0.0071%, 0.0006%, and 0.0017%, respectively, verifying the effectiveness of the online calibration method of the polarization-based solar sensor parameters.

[0071] Table 2

[0072]

[0073] Figure 3 The figure shows the simulation results of online calibration of the polarization sensor parameters based on the terahertz sensor (taking polarization sensor channel 2 as an example). As can be seen from the figure, the light intensity gain coefficient converges at about 200s, the polarization degree coefficient converges at about 120s, and the polarizer installation angle converges at about 300s. Table 3 shows the online calibration results of the polarization sensor parameters based on the terahertz sensor. As can be seen from the table, the convergence calibration errors of the light intensity gain coefficient, polarization degree coefficient, and polarizer installation angle are 0.0104%, 0.0184%, and 0.0046%, respectively, verifying the effectiveness of the online calibration of the polarization sensor parameters based on the terahertz sensor.

[0074] Table 3

[0075]

[0076] The advantages of this invention compared to existing technologies are as follows: It proposes a polarization / thermosensitive online mutual calibration method for the first time, which makes full use of the information from the polarization sensor and the thermosensitive sensor, and realizes online mutual calibration of the parameters of the two without relying on a high-precision turntable, thereby improving the model accuracy and environmental adaptability of the polarization sensor and the thermosensitive sensor in complex scenarios.

[0077] 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 method for online mutual calibration of polarization / polarity sensor parameters.

[0078] 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 method for online mutual calibration of polarization / polarity sensor parameters.

[0079] 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 method for online mutual calibration of polarization / polarization sensor parameters, characterized in that, include: Step 1: Using the polarization sensor's light intensity gain coefficient, polarization degree coefficient, polarizer installation angle, polarizer focal length, polarizer lateral principal point coordinates, and polarizer longitudinal principal point coordinates as system state variables, establish the polarization / polarizer online calibration state equation. Step 2: Using the polarization vector measured by the polarization sensor, establish an online calibration and measurement equation for the polarization-based solar sensor parameters; Using the solar vector measured by the solar sensor, an online calibration and measurement equation for the polarization sensor parameters based on the solar sensor is established; Step 3: Based on the Euclidean distance between the solar image coordinates and the principal point coordinates of the solar sensor, and the Euclidean distance between the solar image coordinates and the principal point coordinates of the solar sensor, establish a confidence discrimination function for polarization measurement and solar sensor measurement, and design an online mutual calibration switching strategy for polarization / solar sensor parameters. Step 4: Based on the state equation established in Step 1, the measurement equation established in Step 2, and the switching strategy established in Step 3, use the unscented Kalman filter method to jointly estimate the system state variables and calibrate the polarization / thermosensitive sensor parameters online.

2. The method for online mutual calibration of polarization / polarity sensor parameters according to claim 1, characterized in that, In step 1, the state variables for the polarization / thermosensitive online calibration are selected as: polarization sensor light intensity gain coefficient, polarization degree coefficient, polarizer installation angle, thermosensitive focal length, thermosensitive lateral principal point coordinates, and thermosensitive longitudinal principal point coordinates. The state equation is set such that the first derivative of the system state variables is zero, and system process noise is introduced to characterize the slow variation characteristics of the parameters.

3. The method for online mutual calibration of polarization / polarity sensor parameters according to claim 1, characterized in that, In step 2, establishing the online calibration measurement equation for the polarization-based solar sensor parameters includes: calculating the solar vector angle and synthesizing the solar vector based on the solar image coordinates measured by the solar sensor, the focal length of the solar sensor to be calibrated, and the principal point coordinates; orthogonally constraining the calculated solar vector with the polarization vector and attitude transformation matrix measured by the polarization sensor, and constructing a first-class measurement equation with the mathematical relationship of the orthogonal constraint as the core.

4. The method for online mutual calibration of polarization / polarity sensor parameters according to claim 1, characterized in that, In step 2, establishing the online calibration measurement equation for the polarization sensor parameters based on the solar sensor includes: calculating the reference polarization vector in the carrier coordinate system based on the solar vector measured by the solar sensor and the observation vector of the polarization sensor; substituting the polarization azimuth angle corresponding to the reference polarization vector into the physical model of the output light intensity of the polarization sensor, and constructing a second type of measurement equation with the output light intensity of each channel of the sensor as the core.

5. The method for online mutual calibration of polarization / polarity sensor parameters according to claim 1, characterized in that, In step 3, the confidence discrimination function and the online mutual calibration switching strategy are specifically as follows: calculate the Euclidean distance between the solar image coordinates and the coordinates of the solar sensor principal point; set a polarization degree threshold and a distance threshold; when the polarization degree is greater than or equal to the polarization degree threshold, select the polarization-based measurement equation and calibrate the solar sensor parameters; when the Euclidean distance is less than the distance threshold, select the solar sensor-based measurement equation and calibrate the polarization sensor parameters.

6. The method for online mutual calibration of polarization / polarity sensor parameters according to claim 5, characterized in that, The polarization degree threshold is set based on the physical characteristic that the measurement accuracy of the polarization sensor increases with the increase of polarization degree.

7. The method for online mutual calibration of polarization / polarity sensor parameters according to claim 5, characterized in that, The distance threshold is set based on the imaging characteristic that the accuracy of the solar sensor measurement decreases as the solar image point deviates from the center of the field of view.

8. The method for online mutual calibration of polarization / polarity sensor parameters according to claim 1, characterized in that, In step 4, the process of joint estimation using the unscented Kalman filter method is as follows: According to the switching strategy, at each moment, the currently effective measurement equation is selected, which together with the state equation constitutes the state estimation system; through the time update and measurement update steps of the unscented Kalman filter, the optimal estimate of the system state variables is obtained recursively, and the parameter calibration is completed.

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 online mutual calibration method for polarization / polarity sensor parameters 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 online mutual calibration method for polarization / polarimetric sensor parameters as described in any one of claims 1-8.

Images