A polarization sensor modeling method considering sunlight interference

Abstract

The present invention relates to a polarization sensor modeling method considering sunlight interference, which comprises the following steps: dividing light incident on the polarization sensor into three parts: linearly polarized light, vertically incident unpolarized light and sunlight; considering the influence of sunlight interference on the polarization sensor based on Malus's law, characterizing the sunlight interference transmission light intensity; based on a long short-term memory network, taking polarization light intensity information, a solar vector, polarization sensor calibration parameters, polarization sensor field angle, inner wall reflection coefficient and diffuse reflection coefficient as input, sunlight interference transmission light intensity and sunlight proportion as output, taking the polarization angle after sunlight interference compensation as a training target, and learning the sunlight interference transmission light intensity equation; comprehensively considering the influence of linear polarized light, vertically incident unpolarized light and sunlight on the polarization sensor, combining the light intensity gain coefficient of the polarization sensor, establishing a polarization sensor model considering sunlight interference, and realizing the calculation of polarization information such as polarization angle and polarization degree.

Description

Technical Field

[0001] The present invention relates to a polarization sensor modeling method that takes sunlight interference into account. A long short-term memory network is used to learn about sunlight interference, and a polarization sensor model that takes sunlight interference into account is established. This method can estimate and compensate for sunlight interference at any time and in any observation direction, thereby improving the angular measurement accuracy of the polarization sensor. Background Art

[0002] Inspired by the navigation mechanisms of animals such as desert ants, bees, and butterflies, biomimetic polarimetric navigation technology has garnered widespread attention. This technology boasts advantages such as immunity to electromagnetic interference and zero error accumulation, and has been successfully applied to fields such as drones, ground robots, and underwater robots. In biomimetic polarimetric navigation systems, polarization sensors play a key role, sensing polarization information in the sky and enabling the inversion and interpretation of navigation information. As the "soul" of a polarization sensor, the polarization sensor model characterizes the sensor's input / output and key parameters, ensuring high-precision polarization sensors. Therefore, designing sophisticated modeling methods for polarization sensors in complex interference environments is crucial for improving sensor performance.

[0003] Currently, researchers have conducted extensive research on polarization sensor modeling. For example, Chinese invention patent CN201810129371.8 ("A method for calibrating multi-source errors of a biomimetic polarization sensor based on unscented Kalman filtering") conducts an in-depth analysis of multi-source errors such as installation error, measurement noise, and scale factor of a polarization sensor, constructs a polarization sensor model, and uses unscented Kalman filtering technology to calibrate and compensate for multi-source errors without relying on high-precision instruments and equipment. Chinese invention patent CN202310212296.2 ("A polarization zero-position calibration system for a polarization sensor") proposes a polarization zero-position calibration system for a polarization sensor. By integrating components such as an integrating sphere, a polarizer, and a turntable, it achieves precise calibration of the zero-degree polarization direction of the polarization sensor, eliminating installation errors in navigation applications. Chinese invention patent CN202010475084.X (“A bionic polarization compass calibration method based on polarization two-dimensional residual information”) proposes a bionic polarization compass calibration method based on polarization two-dimensional residual information. This method introduces the extinction ratio coefficient and polarizer installation error, and improves the accuracy and stability of polarization solution through parameter iterative estimation. However, the above-mentioned polarization sensor modeling method only considers the internal parameter errors of the sensor, and has not fully considered the influence of external interference factors. In actual environments, sunlight interference, as a kind of unpolarized light interference, has a particularly significant impact on polarization sensors. When sunlight is incident on the polarization sensor, there are complex processes such as inner wall reflection and diffuse reflection, which seriously affect the angular measurement accuracy of the polarization sensor. Therefore, how to achieve a fine characterization of sunlight interference, establish a polarization sensor model considering sunlight interference, and improve the angular measurement accuracy of the polarization sensor needs to be studied urgently. Summary of the Invention

[0004] To address the aforementioned issues and overcome the shortcomings of existing technologies, the present invention proposes a polarization sensor modeling method that considers sunlight interference. This method divides the light incident on the polarization sensor into three components: linearly polarized light, vertically incident unpolarized light, and sunlight. Based on Malus's law, the influence of sunlight interference on the polarization sensor is further considered to characterize the sunlight interference transmission intensity. Based on a long short-term memory network (LSTM), the method takes polarization intensity information, the sun vector, polarization sensor calibration parameters, polarization sensor field of view, inner wall reflectance coefficient, and diffuse reflectance as inputs, and sunlight interference transmission intensity and sunlight percentage as outputs. The polarization angle after sunlight interference compensation is used as the training target to learn the sunlight interference transmission intensity equation and sunlight percentage. Next, the effects of linearly polarized light, vertically incident unpolarized light, and sunlight on the polarization sensor are comprehensively considered, and the polarization sensor's light intensity gain coefficient is further combined to establish a polarization sensor model that considers sunlight interference. Finally, based on the established polarization sensor model, the sunlight interference transmission intensity is estimated and compensated, achieving the calculation of the polarization angle and degree of polarization. It can estimate and compensate for the intensity of sunlight interference transmitted at any time and in any observation direction, thereby improving the angle measurement accuracy of the polarization sensor.

[0005] The technical solution of the present invention is: a polarization sensor modeling method considering sunlight interference, which is implemented in the following steps:

[0006] Step (1) divides the light incident on the polarization sensor into three parts: linearly polarized light, vertically incident unpolarized light, and sunlight. The output light intensity of the polarization sensor is expressed as: is the linearly polarized light transmission intensity of the i-th optical path channel, is the vertically incident unpolarized light intensity of the i-th optical path channel, is the sunlight interference transmission intensity of the i-th optical path channel, i=1,2,...,N represents the i-th channel of the polarization sensor, and N is the total number of channels. Characterized by I in Indicates the input light intensity, represents the extinction ratio coefficient of the polarizer, d represents the degree of polarization, represents the polarization angle, θ i Indicates the polarizer installation angle; Characterized by k μ is the proportion of sunlight in the incident light. Further considering the impact of sunlight on the polarization sensor, Characterized by Where T i 、T r and T s They represent the transmittance of direct sunlight, reflected light from the inner wall of the polarization sensor, and diffuse reflected light in the polarizer, respectively.μd and k μr They represent the proportion of direct sunlight and reflected light in sunlight interference, K r and K s are the inner wall reflection and diffuse reflection loss coefficients of the polarization sensor, respectively.

[0007] Step (2): sunlight interference transmitted light intensity in step (1) The long short-term memory network is used for learning. i 、Sun vector s n , polarization sensor calibration parameters C p , polarization sensor field of view v a , polarization sensor inner wall reflection coefficient τ r and diffuse reflectance τ d As training input. Use sunlight to interfere with the transmitted light intensity and the proportion of sunlight in the incident light k μ As output, the polarization angle of sunlight that interferes with the transmitted light intensity will be compensated As a training target. Use the reference navigation information to infer the ideal polarization angle information and learn it as a label and k μ , where Sg(·) represents the established sunlight interference transmission intensity equation.

[0008] Step (3), combined with step (1) and step (2), considering the influence of linear polarized light, vertical incident unpolarized light and sunlight on the polarization sensor, combined with the light intensity gain coefficient of the polarization sensor Build a polarization sensor model that takes sunlight interference into account:

[0009] Step (4): Based on the polarization sensor model established in step (3), the polarized light intensity y at any time and in any observation direction is converted to i 、Sun vector information n , polarization sensor calibration parameters C p , polarization sensor field of view v a , inner wall reflection coefficient τ r and diffuse reflectance τ d As input, the transmitted light intensity of sunlight interference is realized and sunlight ratio k μ The output light intensity of the polarization sensor is used to compensate for the interference of sunlight on the transmitted light intensity, and the polarization information such as polarization angle and polarization degree is calculated.

[0010] Furthermore, in step (1), the light incident on the polarization sensor is divided into three parts: linearly polarized light, vertically incident unpolarized light, and sunlight. The output light intensity of the polarization sensor is expressed as: is the linearly polarized light transmission intensity of the i-th optical path channel, is the vertically incident unpolarized light intensity of the i-th optical path channel, is the sunlight interference transmission intensity of the i-th optical path channel, i=1,2,...,N represents the i-th channel of the polarization sensor, and N is the total number of channels. Characterized by I in Indicates the input light intensity, represents the extinction ratio coefficient of the polarizer, d represents the degree of polarization, represents the polarization angle, θ i Indicates the polarizer installation angle; Characterized by k μ is the proportion of sunlight in the incident light. Further considering the impact of sunlight on the polarization sensor, Characterized by Where T i 、T r and T s They represent the transmittance of direct sunlight, reflected light from the inner wall of the polarization sensor, and diffuse reflected light in the polarizer, respectively. μd and k μr They represent the proportion of direct sunlight and reflected light in sunlight interference, K r and K s are the inner wall reflection and diffuse reflection loss coefficients of the polarization sensor, respectively.

[0011] Furthermore, in step (2), the sunlight interference transmitted light intensity in step (1) is The long short-term memory network is used for learning. i 、Sun vector s n , polarization sensor calibration parameters C p , polarization sensor field of view v a , polarization sensor inner wall reflection coefficient τ r and diffuse reflectance τ d As training input. Use sunlight to interfere with the transmitted light intensity and sunlight accounts for k of the incident light μ As output, the polarization angle of sunlight that interferes with the transmitted light intensity will be compensated As a training target. Use the heading angle, pitch angle and roll angle information provided by high-precision inertial navigation to infer the ideal polarization angle information and learn it as a label and kμ , where Sg(·) represents the established sunlight interference transmission intensity equation.

[0012] Use polarization sensor to obtain polarization light intensity information i , using the solar calendar to calculate the solar vector s n The field of view angle of the inner wall is calculated based on the optical path parameters of the polarization sensor: Where γ is the bottom radius of the inner wall of the polarization sensor, and f is the vertical distance between the top of the inner wall and the photosensitive chip. Based on the material of the polarization sensor, the inner wall reflection coefficient is τ r , diffuse reflection coefficient is τ d .

[0013] To obtain the standard polarization angle First, based on the Rayleigh scattering model, the relationship between the polarization vector and the solar vector is established:

[0014]

[0015] Where the sun vector is represented by s n =[sinA s cosH s cosA s cosH s sinH s ] T 3×1 , the attitude transformation matrix from the carrier coordinate system to the geographic coordinate system is expressed as Polarization vector e b Expressed as:

[0016]

[0017] Furthermore, the standard polarization angle can be obtained through the polarization vector The specific expression is as follows:

[0018]

[0019] in, Represents the posture transformation matrix The value of row i and column j, i, j = 1, 2, 3, A s and H s represent the solar azimuth and altitude, respectively.

[0020] In terms of sunlight interference with transmitted light intensity, polarization intensity information y i 、Sun vector s n , polarization sensor calibration parameters C p and the polarization sensor field of view v a , inner wall reflection coefficient τ rand diffuse reflectance τ d As training input, predict the intensity of sunlight interfering with the transmitted light and obtain the polarization angle after interference compensation. Use the polarization angle obtained by inversion The sunlight interference transmission intensity equation is trained using the ground truth labels. During training, local features are extracted through a one-dimensional convolutional layer, and the temporal dependencies in the data are captured using a long short-term memory network. Finally, the estimation of the sunlight interference transmission intensity is further enhanced through a fully connected layer. The training loss function can be expressed as:

[0021]

[0022] Among them, σ and ρ are weights used to balance the mean square error and standard deviation, and RMSE and STD represent the mean square error and standard deviation of the predicted value and the true value, respectively.

[0023] Using polarization intensity information y i 、Sun vector s n , polarization sensor calibration parameters C p and the polarization sensor field of view v a , inner wall reflection coefficient τ r and diffuse reflectance τ d As input, based on the label value of formula (3) and the loss function of formula (4), the equation of sunlight interference transmission intensity is learned and the proportion of sunlight in the incident light k μ .

[0024] Furthermore, in step (3), in combination with step (1) and step (2), the influence of linearly polarized light, vertically incident unpolarized light, and sunlight on the polarization sensor is comprehensively considered to establish the relationship between the output light intensity and the incident light intensity after the action of the polarization device:

[0025]

[0026] Further combined with the light intensity gain coefficient of the polarization sensor Build a polarization sensor model that takes sunlight interference into account:

[0027]

[0028] Furthermore, in step (4), based on the polarization sensor model established in step (3), the polarized light intensity y at any time and in any observation direction is converted to i 、Sun vector information n , polarization sensor calibration parameters C p , polarization sensor field of view v a , inner wall reflection coefficient τ r and diffuse reflectance τd As input, the transmitted light intensity of sunlight interference is realized and sunlight accounts for k of the incident light μ The output light intensity of the polarization sensor is compensated for the interference of sunlight on the transmitted light intensity, and the polarization information such as polarization angle and polarization degree are calculated. After compensation, the output light intensity of the polarization sensor is

[0029]

[0030] Combining formulas (6) and (7), the polarization sensor model considering sunlight interference is expressed in matrix form: in Represent the output light intensity of the four channels of the sensor, k represents the kth sampling point, and n represents the total number of sampling points. is the polarization sensor parameter matrix, and the polarization angle is The degree of polarization is d o =[d o1 ,d o2 ,...,d on ] n×1 , is a vector containing the polarization angle and degree of polarization. The least squares estimate of x is as follows:

[0031]

[0032] Polarization angle after sunlight interference compensation Degree of polarization d o As follows:

[0033]

[0034] in, and Represents vectors Items 1, 2, and 3 of .

[0035] The advantages of the present invention over the prior art are: a polarization sensor modeling method that takes sunlight interference into account is proposed, and a long short-term memory network is used to achieve fine characterization and learning of sunlight interference. This can achieve estimation and compensation of sunlight interference at any time and in any observation direction, thereby improving the angular measurement accuracy of the polarization sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a polarization sensor modeling method considering sunlight interference according to the present invention; DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Polarization sensors primarily sense polarized sunlight scattered by the atmosphere. However, due to their limited viewing angle, interference from sunlight can also enter the sensor's optical path. This paper proposes a polarization sensor modeling method that accounts for sunlight interference. Using a long-short-term memory network (LSTM) approach, an equation characterizing the intensity of transmitted sunlight interference is constructed, and the proportion of sunlight in the incident light is predicted. This model establishes a polarization sensor model that accounts for sunlight interference. This model estimates and compensates for the intensity of transmitted sunlight interference at any time and from any observation direction, improving the angular measurement accuracy of the polarization sensor.

[0039] The specific implementation steps of the present invention are as follows:

[0040] Step (1) divides the light incident on the polarization sensor into three parts: linearly polarized light, vertically incident unpolarized light, and sunlight. The output light intensity of the polarization sensor is expressed as: is the linearly polarized light transmission intensity of the i-th optical path channel, is the vertically incident unpolarized light intensity of the i-th optical path channel, is the sunlight interference transmission intensity of the i-th optical path channel, i=1,2,...,N represents the i-th channel of the polarization sensor, and N is the total number of channels. Characterized by I in Indicates the input light intensity, represents the extinction ratio coefficient of the polarizer, d represents the degree of polarization, represents the polarization angle, θ i Indicates the polarizer installation angle; Characterized by k μ is the proportion of sunlight in the incident light. Further considering the impact of sunlight on the polarization sensor, Characterized by Where T i 、T r and T s They represent the transmittance of direct sunlight, reflected light from the inner wall of the polarization sensor, and diffuse reflected light in the polarizer, respectively. μd and k μr They represent the proportion of direct sunlight and reflected light in sunlight interference, K r and K sare the inner wall reflection and diffuse reflection loss coefficients of the polarization sensor, respectively.

[0041] Step (2): sunlight interference transmitted light intensity in step (1) The long short-term memory network is used for learning. i 、Sun vector s n , polarization sensor calibration parameters C p , polarization sensor field of view v a , polarization sensor inner wall reflection coefficient τ r and diffuse reflectance τ d As training input. Use sunlight to interfere with the transmitted light intensity and the proportion of sunlight in the incident light k μ As output, the polarization angle of sunlight that interferes with the transmitted light intensity will be compensated As a training target, the ideal polarization angle information is inferred by using the heading, pitch and roll angle information provided by the high-precision inertial navigation system (INS900A). and learn it as a label and k μ , where Sg(·) represents the established sunlight interference transmission intensity equation.

[0042] Use polarization sensor to obtain polarization light intensity information i , using the solar calendar to calculate the solar vector s n The field of view angle of the inner wall is calculated based on the optical path parameters of the polarization sensor: Where γ is the bottom radius of the inner wall of the polarization sensor, and f is the vertical distance between the top of the inner wall and the photosensitive chip. Based on the material of the polarization sensor, the inner wall reflection coefficient is τ r , diffuse reflection coefficient is τ d .

[0043] To obtain the standard polarization angle First, based on the Rayleigh scattering model, the relationship between the polarization vector and the solar vector is established:

[0044]

[0045] Where the sun vector is represented by s n =[sinA s cosH s cosA s cosH s sinH s ] T 3×1 , the attitude transformation matrix from the carrier coordinate system to the geographic coordinate system is expressed as Polarization vector e b Expressed as:

[0046]

[0047] Furthermore, the standard polarization angle can be obtained through the polarization vector The specific expression is as follows:

[0048]

[0049] in, Represents the posture transformation matrix The value of row i and column j, i, j = 1, 2, 3, A s and H s represent the solar azimuth and altitude, respectively.

[0050] In terms of sunlight interference with transmitted light intensity, polarization intensity information y i 、Sun vector s n , polarization sensor calibration parameters C p and the polarization sensor field of view v a , inner wall reflection coefficient τ r and diffuse reflectance τ d As training input, predict the intensity of sunlight interfering with the transmitted light and obtain the polarization angle after interference compensation. Use the polarization angle obtained by inversion The equation for the intensity of sunlight interference transmission is trained using the ground truth labels. During training, local features are extracted through a one-dimensional convolutional layer, and the temporal dependencies in the data are captured using a long short-term memory network. Finally, the estimation of the intensity of sunlight interference transmission is further enhanced through a fully connected layer. The training loss function can be expressed as:

[0051]

[0052] Among them, σ and ρ are weights used to balance the mean square error and standard deviation, and RMSE and STD represent the mean square error and standard deviation of the predicted value and the true value, respectively.

[0053] Using polarization intensity information y i 、Sun vector s n , polarization sensor calibration parameters C p and the polarization sensor field of view v a , inner wall reflection coefficient τ r and diffuse reflectance τ d As input, based on the label value of formula (3) and the loss function of formula (4), the equation of sunlight interference transmission intensity is learned and the proportion of sunlight in the incident light k μ .

[0054] Step (3): Combine steps (1) and (2), comprehensively consider the effects of linearly polarized light, vertically incident unpolarized light, and sunlight on the polarization sensor, and establish the relationship between the output light intensity and the incident light intensity after the polarization device is applied:

[0055]

[0056] Further combined with the light intensity gain coefficient of the polarization sensor Build a polarization sensor model that takes sunlight interference into account:

[0057]

[0058] Step (4): Based on the polarization sensor model established in step (3), the polarized light intensity y at any time and observation direction is converted to i 、Sun vector information n , polarization sensor calibration parameters C p , polarization sensor field of view v a , inner wall reflection coefficient τ r and diffuse reflectance τ d As input, the transmitted light intensity of sunlight interference is realized and sunlight accounts for k of the incident light μ The output light intensity of the polarization sensor is compensated for the interference of sunlight on the transmitted light intensity, and the polarization angle, polarization degree and other information are calculated. After compensation, the output light intensity of the polarization sensor is

[0059]

[0060] Combining formulas (6) and (7), the polarization sensor model considering sunlight interference is expressed in matrix form: in Represent the output light intensity of the four channels of the sensor, k represents the kth sampling point, and n represents the total number of sampling points. is the polarization sensor parameter matrix, and the polarization angle is The degree of polarization is d o =[d o1 ,d o2 ,...,d on ] n×1 , is a vector containing the polarization angle and degree of polarization. The least squares estimate of x is as follows:

[0061]

[0062] Polarization angle after sunlight interference compensation Degree of polarization d o As follows:

[0063]

[0064] in, and Represents vectors Items 1, 2, and 3 of .

[0065] Although the above describes the illustrative specific embodiments of the present invention to facilitate understanding of the present invention by those skilled in the art, and it should be clear that the present invention is not limited to the scope of the specific embodiments, it is obvious to those skilled in the art that as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

Claims

1. A polarization sensor modeling method considering sunlight interference, characterized in that: The implementation steps are as follows: Step (1) divides the light incident on the polarization sensor into three parts: linearly polarized light, vertically incident unpolarized light, and sunlight. The output light intensity of the polarization sensor is expressed as: , For the i The linear polarized light transmission intensity of each optical path channel is For the i The vertical incident unpolarized transmitted light intensity of each optical path channel is: For the i The sunlight interference transmitted light intensity of each optical path channel is Indicates the polarization sensor i channels, N is the total number of channels; where Characterized by , Indicates the input light intensity, represents the extinction ratio coefficient of the polarizer, represents the degree of polarization, represents the polarization angle, Indicates the polarizer installation angle; Characterized by , is the proportion of sunlight in the incident light; further considering the impact of sunlight on the polarization sensor, Characterized by ,in 、 and They represent the transmittance of direct sunlight, reflected light from the inner wall of the polarization sensor, and diffuse reflected light in the polarizer. and They represent the proportion of direct sunlight and reflected light in sunlight interference respectively. and represent the inner wall reflection and diffuse reflection loss coefficients of the polarization sensor respectively; Step (2): for the sunlight interference transmitted light intensity in step (1) , using long short-term memory network for learning; using polarization intensity information , Sun Vector , polarization sensor calibration parameters , polarization sensor field of view , polarization sensor inner wall reflection coefficient and diffuse reflectance As training input; use sunlight to interfere with the transmitted light intensity and the proportion of sunlight in incident light As output, the polarization angle of sunlight that interferes with the transmitted light intensity will be compensated As a training target; use the reference navigation information to infer the ideal polarization angle information , and use it as a label to learn and ,in It represents the established equation of sunlight interference transmitted light intensity; Step (3), combined with step (1) and step (2), considering the effects of linear polarized light, vertically incident unpolarized light and sunlight on the polarization sensor, combined with the light intensity gain coefficient of the polarization sensor , establish a polarization sensor model considering sunlight interference: ; Step (4): Based on the polarization sensor model established in step (3), the polarization intensity at any time and in any observation direction is , Sun vector information , polarization sensor calibration parameters , polarization sensor field of view , inner wall reflection coefficient and diffuse reflectance As input, the transmitted light intensity of sunlight interference is realized and sunlight ratio The prediction of the polarization information is completed by compensating the sunlight interference transmitted light intensity in the polarization sensor output light intensity.

2. The polarization sensor modeling method considering sunlight interference according to claim 1, characterized in that: In the step (2), the sunlight interference transmitted light intensity in step (1) , using long short-term memory network for learning; using polarization intensity information , Sun Vector , polarization sensor calibration parameters , polarization sensor field of view , polarization sensor inner wall reflection coefficient and diffuse reflectance As training input; Interference of transmitted light intensity with sunlight and sunlight as a percentage of incident light As output, the polarization angle of sunlight that interferes with the transmitted light intensity will be compensated As a training target; use the heading, pitch and roll angle information provided by high-precision inertial navigation to infer the ideal polarization angle information , and use it as a label to learn and ,in It represents the established equation of sunlight interference transmitted light intensity; Using polarization sensors to obtain polarized light intensity information , using the solar calendar to calculate the solar vector ; Calculate the field of view angle of the inner wall based on the optical path parameters of the polarization sensor: ,in, γ is the bottom radius of the inner wall of the polarization sensor, f is the vertical distance between the top of the inner wall and the photosensitive chip; based on the material of the polarization sensor, the inner wall reflection coefficient is , the diffuse reflection coefficient is ; To obtain the standard polarization angle First, based on the Rayleigh scattering model, the relationship between the polarization vector and the solar vector is established: The sun vector is expressed as , the attitude transformation matrix from the carrier coordinate system to the geographic coordinate system is expressed as , polarization vector Expressed as: Furthermore, the standard polarization angle can be obtained through the polarization vector , specifically expressed as follows: in, Represents the posture transformation matrix No. i Row, No. j The value of the column, i , j =1,2,3, and Represent the solar azimuth and altitude respectively; Using polarized light intensity information to study sunlight interference with transmitted light intensity , Sun Vector , polarization sensor calibration parameters and polarization sensor field of view , inner wall reflection coefficient and diffuse reflectance As training input, predict the intensity of sunlight interfering with the transmitted light and obtain the polarization angle after interference compensation. , using the polarization angle obtained by inversion The sunlight interference transmission intensity equation is trained as the true value label. During the training process, local features are extracted through a one-dimensional convolutional layer, and the time dependency in the data is captured using a long short-term memory network. Finally, the estimation ability of the sunlight interference transmission intensity is further enhanced through a fully connected layer. The training loss function can be expressed as: in, and It is the weight used to balance the mean square error and standard deviation. RMSE and STD represent the mean square error and standard deviation of the predicted value and the true value respectively. Using polarization intensity information , Sun Vector , polarization sensor calibration parameters and polarization sensor field of view , inner wall reflection coefficient and diffuse reflectance As input, based on the label value of formula (3) and the loss function of formula (4), the equation of sunlight interference transmission intensity is learned and the proportion of sunlight in incident light .

3. The polarization sensor modeling method considering sunlight interference according to claim 1, characterized in that: In step (3), in combination with step (1) and step (2), the influence of linear polarized light, vertically incident unpolarized light and sunlight on the polarization sensor is comprehensively considered to establish the relationship between the output light intensity and the incident light intensity after the action of the polarization device: Further combined with the light intensity gain coefficient of the polarization sensor , establish a polarization sensor model considering sunlight interference:

4. The polarization sensor modeling method considering sunlight interference according to claim 1, characterized in that: The step (4) is based on the polarization sensor model established in step (3), and the polarization intensity at any time and any observation direction is calculated. , Sun vector information , polarization sensor calibration parameters , polarization sensor field of view , inner wall reflection coefficient and diffuse reflectance As input, the transmitted light intensity of sunlight interference is realized and sunlight as a percentage of incident light The prediction of the polarization sensor output light intensity is used to compensate for the sunlight interference transmitted light intensity, and the polarization information is solved. After compensation, the polarization sensor output light intensity is : Combining formulas (6) and (7), the polarization sensor model considering sunlight interference is expressed in matrix form: ,in , Respectively represent the output light intensity of the four channels of the sensor, k Indicates the k sampling points, n Indicates the total number of sampling points; is the polarization sensor parameter matrix, and the polarization angle is , the polarization degree is , is a vector containing the polarization angle and degree of polarization; The least squares estimate of is as follows: Polarization angle after sunlight interference compensation , polarization degree As follows: in, 、 and Represents vectors Items 1, 2, and 3 of .

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