Rain medium polarized electromagnetic wave propagation matrix inversion method and device by library

Abstract

This invention relates to the field of electromagnetic wave signal processing, and provides a method and apparatus for library-by-library inversion of the propagation matrix of rain-medium polarized electromagnetic waves. The method includes obtaining the relationship between the electromagnetic waves transmitted by a dual-polarization antenna and the induced voltage when the transmission medium is rain; and uniformly dividing the rain region according to the distance library. N The process involves: constructing observation equations for each rain region and obtaining the covariance matrix of these equations, thus obtaining the observation matrix; constructing state equations for each rain region and obtaining the covariance matrix and state transition matrix of these state equations; obtaining the linear state vector of the input layer rain region based on the state equations, calculating the predicted state and prediction error covariance, and calculating the Kalman gain; updating the predicted state and prediction error covariance based on the Kalman gain; traversing all rain regions to obtain the optimal estimated propagation matrix for each rain region; and finally, obtaining and outputting the optimal estimated rain region propagation matrix. This invention effectively reduces the impact of noise on the inversion results, achieving real-time and high-precision inversion of the rainfall propagation matrix.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic wave signal processing, and more particularly to a method and apparatus for library-by-library inversion of the propagation matrix of rain-medium polarized electromagnetic waves. Background Technology

[0002] Electromagnetic waves are the only efficient carrier discovered by humankind for information transmission and sensing across land, sea, air, and space. With the rapid development of information technology, microwave spectrum resources are becoming increasingly scarce, and electromagnetic waves are continuously expanding into higher frequency bands. However, the increasing frequency bands make the impact of meteorological phenomena on electromagnetic wave propagation more significant, especially the refraction, absorption, and scattering effects of meteorological particles such as rain, fog, and snow. Research shows that rainfall is the most significant limiting factor for electromagnetic wave propagation above 10 GHz, affecting not only signal attenuation but also altering the polarization characteristics of electromagnetic waves. Therefore, in-depth research into the propagation characteristics of electromagnetic waves under complex meteorological conditions, particularly the characterization of polarization effects, is of great importance for improving the detection performance of radar systems.

[0003] Through long-term field experiments and theoretical analysis, scholars have established various models for calculating rainfall attenuation, which can be categorized into empirical models and physical models. However, due to significant differences in precipitation characteristics across regions, empirical models often require continuous adjustment and verification during application in different areas, making them difficult to apply broadly. Physical models, on the other hand, start with the microphysical characteristics of rainfall, studying the influence of raindrop spectrum, raindrop shape, and precipitation particle type on electromagnetic wave attenuation. Factors such as raindrop size, shape, tilt angle, and raindrop spectrum distribution all significantly affect polarized electromagnetic waves. However, current physical models are overly idealistic, neglecting the polarization effect of rainfall particles, which limits their applicability in real-world rainfall scenarios.

[0004] Polarization is another important piece of information for electromagnetic waves besides the time, frequency, and spatial domains. Utilizing polarization information can invert the composition, size, structure, and other microphysical properties of meteorological targets, significantly improving their detection, identification, and classification. The propagation matrix is ​​a core parameter describing the propagation characteristics of electromagnetic waves in a propagation medium. Using polarized echo data, it is hoped that the precipitation propagation matrix can be inverted. However, due to the complexity of the propagation environment and the uncertainty of target characteristics, electromagnetic waves are subject to interference from noise and other factors during propagation. These interference factors greatly affect the estimation accuracy of the precipitation propagation matrix. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a library-by-library inversion method and apparatus for rain-medium polarized electromagnetic wave propagation matrix.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: On one hand, the present invention provides a library-by-library inversion method for the propagation matrix of rain-medium polarized electromagnetic waves, comprising the following steps: When the transmission medium is rain, obtain the relationship between the electromagnetic waves emitted by the dual polarization antenna and the induced voltage. The rain zone along the electromagnetic wave propagation path is evenly divided according to the distance from the reservoir. N layer; The observation equations for each rain region are constructed based on the relationship between the electromagnetic waves transmitted by the dual-polarization antenna and the induced voltage, and the covariance matrix of the observation equations is obtained. The observation equations are then transformed into standard form through matrix straightening transformation to obtain the observation matrix. Construct the state equations for each rain zone, and obtain the covariance matrix and state transition matrix of the state equations; The linear state vector of the rain region in the input layer is obtained based on the state equation; The predicted state and prediction error covariance are obtained based on the linear state vector of the rain region in the input layer, and the Kalman gain is calculated. The predicted state and prediction error covariance are updated according to the Kalman gain until all rain regions are traversed to obtain the optimal estimated propagation matrix for each rain region. Calculate the product of the propagation matrices of each rain zone based on the optimal estimated propagation matrix, obtain the optimal estimated propagation matrix of the rain zone, and output it.

[0007] Furthermore, the relationship between the electromagnetic wave emitted by the dual-polarization antenna and the induced voltage is expressed by the following formula: ; in, The induced voltage for receiving horizontal polarization of the antenna; The induced voltage for receiving the vertical polarization of the antenna; Horizontally polarized electromagnetic waves emitted by a dual-polarization antenna; Vertically polarized electromagnetic waves emitted by a dual-polarization antenna; This represents the propagation matrix of the rain region along the electromagnetic wave propagation path. The wavelength of electromagnetic waves; Antenna gain; The distance from the target to the radar; Let be the target polarization scattering matrix.

[0008] Furthermore, the observation equation is expressed by the following formula: ; in, For the first k The echo electric field at the distance of the layered rain zone from the reservoir; For the first k Linear state vector of the stratified rain zone; For the first kThe propagation matrix of the stratified rain region; For the first k Observational noise in the layered rain zone.

[0009] Furthermore, the observation matrix is ​​obtained according to the following steps: The observation equation is transformed into its standard form using matrix straightening transformation: ; in, This is the product of the propagation matrices of the rain regions in the first k-1 layers; It is the tensor product; Obtain the k Observation matrix of the stratified rain region : .

[0010] Furthermore, the state equation is expressed by the following formula: ; in, This is the state transition matrix; For the first k Process noise in the stratified rain zone; For the first k The linear state vector of the rain zone at layer -1.

[0011] Furthermore, the predicted state is obtained according to the following formula: ; in, For the first k Predicted values ​​for the state of the stratus rain zone; For the first k Estimated value of the rain zone state at level -1; The prediction error covariance is obtained according to the following formula: ; in, For the first k Prior covariance error in stratified rain regions; For the first k Posterior covariance error of the rain zone at level -1; For the first k The covariance matrix of process noise in the stratified rain region; The Kalman gain is obtained according to the following formula: ; in, For the first k Kalman gain in the stratified rain region; For the first k The covariance matrix of observation noise in the layered rain region.

[0012] Furthermore, the predicted state and prediction error covariance are updated according to the following formula: ; ; in, For the first k Estimates of the state of the stratus rain zone; This is the updated error covariance.

[0013] Furthermore, the propagation matrix of each rain zone is calculated using the Oguchi method.

[0014] Furthermore, the product of the rain area propagation matrix is ​​expressed as: ; in, This is the product of the rain propagation matrix; For the first N The optimal propagation matrix for the layered rain region.

[0015] On the other hand, the present invention provides a library-by-library inversion device for the propagation matrix of rain-medium polarized electromagnetic waves, comprising: The first module is used to obtain the relationship between the electromagnetic waves emitted by the dual polarization antenna and the induced voltage when the transmission medium is rain. The second module is used to uniformly divide the rain zone along the electromagnetic wave propagation path according to the distance from the target area. N layer; The third module is used to construct the observation equations for each rain zone based on the relationship between the electromagnetic waves transmitted by the dual-polarization antenna and the induced voltage, and to obtain the covariance matrix of the observation equations; the observation equations are then transformed into standard form through matrix straightening transformation to obtain the observation matrix; The fourth module is used to construct the state equations for each rain zone and obtain the covariance matrix and state transition matrix of the state equations. The fifth module is used to obtain the linear state vector of the rain region in the input layer based on the state equation; The sixth module is used to obtain the predicted state and prediction error covariance based on the linear state vector of the rain area in the input layer, and to calculate the Kalman gain; the predicted state and prediction error covariance are updated according to the Kalman gain until all rain areas are traversed to obtain the optimal estimated propagation matrix for each rain area; The seventh module is used to calculate the product of the propagation matrices of each rain zone based on the optimal estimated propagation matrix of each rain zone, obtain the optimal estimated propagation matrix of the rain zone, and output it.

[0016] Compared with the prior art, the beneficial technical effects of the present invention are as follows: The rain-medium polarized electromagnetic wave propagation matrix inversion method and apparatus provided by this invention constructs observation equations and converts them into standard form through matrix stretching transformation, thereby facilitating direct processing by Kalman filtering.

[0017] This invention achieves optimal state estimation by establishing state equations and observation equations, combining the state model with observation data; it effectively reduces the impact of noise on the inversion results, and realizes real-time, high-precision inversion of the rainfall propagation matrix. Based on the echo data of each rain zone of the dual-polarized radar, the effective inversion of the rainfall propagation matrix is ​​achieved through iterative recursion; moreover, this invention can exhibit high inversion accuracy under different signal-to-noise ratios and can adapt to different rainfall scenarios.

[0018] This invention can be widely applied in the fields of weather radar, communication systems and remote sensing, providing reliable technical support for target detection, identification and classification. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0020] Figure 1 A schematic diagram of a library-by-library inversion method for rain-medium polarized electromagnetic wave propagation matrix provided in one embodiment; Figure 2 A schematic diagram of rain stratification propagation provided in one embodiment; Figure 3 Here is a flowchart of an embodiment for simulating electromagnetic wave propagation under rainfall conditions; Figure 4 This is a diagram showing the estimation results of the propagation matrix of each rain area under different signal-to-noise ratios, as provided in one embodiment. Figure 4 (a) is a graph showing the estimation results of the propagation matrix for each rain zone when the signal-to-noise ratio is 40dB. Figure 4 (b) is a graph showing the estimation results of the propagation matrix for each rain zone when the signal-to-noise ratio is 30dB. Figure 4 (c) is a graph showing the estimation results of the propagation matrix for each rain zone when the signal-to-noise ratio is 20dB. Figure 4 (d) shows the estimation results of the propagation matrix of each rain zone when the signal-to-noise ratio is 10dB; Figure 5 This is a non-uniform rainfall echo data obtained from WRF simulation, provided as an example. Figure 6 This is a diagram showing the results of estimating the propagation matrix of each rain zone based on WRF rainfall echo data, as provided in one embodiment. Figure 6 (a) is a diagram showing the estimation results of the propagation matrix of each rain zone at azimuth 1. Figure 6 (b) is a diagram showing the estimation results of the propagation matrix of each rain zone at azimuth 2; Figure 7 An experimental scenario diagram provided for one embodiment; Figure 8 The diagram shows the calculation results of the single- and two-way propagation matrix provided in one embodiment. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] Reference Figure 1 One embodiment provides a library-by-library inversion method for the propagation matrix of rain-medium polarized electromagnetic waves, comprising the following steps: When the transmission medium is rain, obtain the relationship between the electromagnetic waves emitted by the dual polarization antenna and the induced voltage. The rain zone along the electromagnetic wave propagation path is evenly divided according to the distance from the reservoir. N layer; The observation equations for each rain region are constructed based on the relationship between the electromagnetic waves transmitted by the dual-polarization antenna and the induced voltage, and the covariance matrix of the observation equations is obtained. The observation equations are then transformed into standard form through matrix straightening transformation to obtain the observation matrix. Construct the state equations for each rain zone, and obtain the covariance matrix and state transition matrix of the state equations; The linear state vector of the rain region in the input layer is obtained based on the state equation; The predicted state and prediction error covariance are obtained based on the linear state vector of the rain region in the input layer, and the Kalman gain is calculated. The predicted state and prediction error covariance are updated according to the Kalman gain until all rain regions are traversed to obtain the optimal estimated propagation matrix for each rain region. Calculate the product of the propagation matrices of each rain zone based on the optimal estimated propagation matrix, obtain the optimal estimated propagation matrix of the rain zone, and output it.

[0023] The relationship between the electromagnetic wave emitted by the dual-polarization antenna and the induced voltage is expressed by the following formula: ; in, The induced voltage for receiving horizontal polarization of the antenna; The induced voltage for receiving the vertical polarization of the antenna; Horizontally polarized electromagnetic waves emitted by a dual-polarization antenna; Vertically polarized electromagnetic waves emitted by a dual-polarization antenna; This represents the propagation matrix of the rain region along the electromagnetic wave propagation path. These are the propagation matrix components under horizontal transmission and horizontal reception. These are the components of the propagation matrix under horizontal transmission and vertical reception. These are the propagation matrix components under vertical transmission and horizontal reception. These are the components of the propagation matrix under vertical transmission and vertical reception. The wavelength of electromagnetic waves; Antenna gain; The distance from the target to the radar; The target polarization scattering matrix, These are the scattering matrix components under horizontal transmission and horizontal reception. These are the scattering matrix components under horizontal transmission and vertical reception. These are the scattering matrix components under vertical transmission and horizontal reception. These are the scattering matrix components under vertical transmission and vertical reception.

[0024] Reference Figure 2 The rain zone along the electromagnetic wave propagation path is evenly divided according to the distance from the reservoir. N If there are multiple layers, then the propagation matrix of the rain region can be expressed as the product of the propagation matrices of each rain region layer, i.e. ,in, Indicates the first k The propagation matrix of the stratified rain region; Figure 2 middle, , Indicates the first k The distance between the center of the rain area and the radar transmitter. .

[0025] In one embodiment, the observation equation is expressed by the following formula: ; in, For the first k The echo electric field at the distance of the layered rain zone from the reservoir; For the first k Linear state vector of the stratified rain zone; For the first k The propagation matrix of the stratified rain region; For the first k Observational noise in the layered rain zone.

[0026] The observation matrix is ​​obtained by following these steps: The observation equation is transformed into its standard form using matrix straightening transformation: ; in, This is the product of the propagation matrices of the rain regions in the first k-1 layers; It is the tensor product; Obtain the k Observation matrix of the stratified rain region : .

[0027] In the observation equation, The presence of propagation matrices on both sides does not conform to the standard form of the observation equation, making direct processing by Kalman filtering difficult. Therefore, a matrix straightening transformation is used to convert the observation equation into its standard form.

[0028] The state equation is expressed by the following formula: ; in, The state transition matrix is ​​used to characterize the state relationship between adjacent rain zones. Based on the similarity of the scattering matrices between adjacent distance cells, the state transition matrix can be the identity matrix. To represent; For the first k Process noise in the stratified rain zone; For the first k The linear state vector of the rain zone at layer -1.

[0029] The predicted state is obtained according to the following formula: ; in, For the first k Predicted values ​​for the state of the stratus rain zone; For the first k Estimated value of the rain zone state at level -1; The prediction error covariance is obtained according to the following formula: ; in, For the first k Prior covariance error in stratified rain regions; For the first k Posterior covariance error of the rain zone at level -1; For the first k The covariance matrix of process noise in the stratified rain region; This can be expressed by the following formula: ; in, For the first k The ratio of differential phase in the stratified rain region, For the firstk The original differential reflectance of the stratus rain region This represents the identity matrix. Since the differential reflectance obtained during actual observation is not the original value of the rain area, but rather a result affected by the rain propagation effect, the differential reflectance measured by radar needs to be corrected to eliminate errors caused by the propagation effect. ; in, Indicates the number measured by radar. k Differential reflectance in stratus rain regions and They represent The inversion values ​​of the propagation matrix elements are obtained under HH and VV polarization modes. Since the propagation matrix result in a single Kalman inversion may contain randomness, multiple iterations are used until... and The residual values ​​tend to converge, and are used as... The propagation matrix element values ​​are being corrected.

[0030] The Kalman gain is obtained according to the following formula: ; in, For the first k Kalman gain in the stratified rain region; For the first k The covariance matrix of observation noise in the layered rain region.

[0031] When the horizontal and vertical polarization channels are independent of each other Expressed using the following formula: ; in, Boltzmann's constant, For radar system noise temperature, For the noise bandwidth of the radar receiver, Represents the identity matrix.

[0032] Update the predicted state and prediction error covariance according to the following formula: ; ; in, For the first k Estimates of the state of the stratus rain zone; This is the updated error covariance.

[0033] After traversing all rain regions, the optimal estimated propagation matrix for each rain region is obtained. The product of the propagation matrices of each rain region is then calculated to obtain the optimal estimated propagation matrix for that rain region, i.e., the optimal estimated propagation matrix. , and output.

[0034] The optimal estimation of the rainfall propagation matrix is ​​achieved by iterating the prediction model and observation data.

[0035] Specifically, the propagation matrix of each rain zone is calculated according to the Oguchi method. Calculate using the following formula: ; ; in, For the distance of propagation; The imaginary unit; The composite wavenumber for horizontal polarization; The composite wavenumber for vertical polarization; The wavenumber of electromagnetic waves under vacuum conditions; For coherent power matrix, These are the coherent power matrix components under horizontal transmission and horizontal reception. These are the components of the coherent power matrix under vertical transmission and vertical reception. These are the components of the coherent power matrix under vertical transmission and horizontal reception. These are the components of the coherent power matrix under horizontal transmission and vertical reception. and The relevant power matrix angle; Let be the forward scattering matrix of the rain region, and let be the backward scattering matrix of the rain region. The relationship is .

[0036] To verify the performance of the present invention, simulation experiments were conducted to verify its performance under different rainfall scenarios.

[0037] The simulation experiment employed a joint simulation method combining meteorological parameters and electromagnetic calculations to model and analyze the propagation characteristics of polarized electromagnetic waves under rainfall scenarios. (Refer to...) Figure 3 First, the rainfall parameters of the simulation scenario are set, and the raindrop spectrum parameters are dynamically generated by combining the Gamma distribution model. Then, the polarization scattering characteristics of raindrops are simulated by using the T matrix method to consider the axial ratio characteristics of non-spherical raindrops. Finally, a polarization electromagnetic wave layered propagation model under rainfall conditions is constructed based on the Oguchi layered propagation theory, and polarization echo data of different rain areas are obtained by simulation.

[0038] In error analysis, the root mean square error (RMSE) is introduced as a quantitative indicator of inversion accuracy, and the inversion results are quantitatively evaluated through RMSE.

[0039] Simulation results under uniform rainfall scenario are as follows Figure 4 As shown, without Kalman filtering, the error results of the propagation matrix of each rain zone show a significant increasing trend with the increase of the number of rain zone iterations. Figure 4 As shown in (b), when the signal-to-noise ratio is 30dB, the RMSE of the propagation matrix in the rain region of the 20th layer reaches 2.415. Figure 4 As shown in (c), when the signal-to-noise ratio (SNR) is 20 dB, the RMSE of the propagation matrix in the rain region of layer 80 reaches 15.891, which significantly exceeds the acceptable error threshold for engineering applications. This indicates that under conditions of low SNR and high iteration count, traditional iterative algorithms can no longer guarantee the estimation accuracy of the propagation matrix. The rapid growth of the inversion error stems from the cumulative effect of noise during the iteration process. The product relationship of the propagation matrices of each layer leads to a nonlinear increase in noise error during the iteration process, and the error propagation function exhibits a near-exponential growth trend.

[0040] The method described in this invention significantly improves the numerical stability of the propagation matrix inversion error. After using Kalman filtering, the error accumulation effect caused by noise is significantly suppressed, and the RMSE of the rain area propagation matrix tends to stabilize with the increase of the number of iterations. Figure 4 As shown in (c), when the signal-to-noise ratio is 20dB, the RMSE of the propagation matrix in the rain region of the 80th layer is 0.0067. Figure 4 As shown in (d), when the signal-to-noise ratio is 10dB, the RMSE of the propagation matrix of the rain area in layer 80 is 0.0095, which can accurately retrieve the propagation matrix of the rain area. Experimental results show that the inversion method described in this invention can control the root mean square error of the inversion error to below 0.01 for a rain area with a propagation distance of 10km when the signal-to-noise ratio is not less than 10dB.

[0041] Simulation results under non-uniform rainfall scenarios are as follows Figure 5 As shown, dual-polarized echo data for a non-uniform rainfall scenario are obtained based on WRF simulation. From Figure 6 The error results show that for the precipitation echo data at azimuth 1, such as Figure 6 As shown in (a), using the inversion method described in this invention, the overall RMSE of the propagation matrix of each rain area can be controlled below 0.012; for the rainfall echo data at azimuth 2, as Figure 6As shown in (b), the overall RMSE of the propagation matrix for each rain region can be controlled below 0.013. This result indicates that, for non-uniform rainfall scenarios, the RMSE of the rainfall propagation matrix inversion using this invention can still be controlled below 0.015, and the propagation matrix of the rain region can be inverted relatively accurately. This verifies the effectiveness and robustness of the algorithm in inverting the rainfall propagation matrix under non-uniform rainfall scenarios.

[0042] The simulation experiments above show that the present invention is effective under complex rainfall conditions. In the case of uniform rain area, the RMSE of the propagation matrix inversion is below 0.01; in the case of non-uniform rain area, the RMSE of the propagation matrix inversion is below 0.015, indicating that the present invention can accurately invert the propagation matrix of different rain areas.

[0043] In one embodiment, to verify the effectiveness of the invention in actual rainfall scenarios, one-way and two-way rainfall experiments were conducted. Figure 7 The experimental scenario diagram shows a polarimetric parabolic weather radar deployed at point A in a vehicle-mounted configuration, with a radar calibrator located at point B. The straight-line distance between the two is 8.6 km, and the height difference is 0.2 km.

[0044] The single-pass experiment is designed with the calibrator transmitting electromagnetic waves and the parabolic radar receiving them; the double-pass experiment is designed with the parabolic radar transmitting and receiving independently. (Refer to...) Figure 8 The values ​​are the modulo values ​​of the rainfall propagation matrix obtained from single-pass and double-pass calculations. The result of the single-pass experiment is used as the true value of the propagation matrix, and the result of the double-pass experiment is used as the estimated value of the propagation matrix. It can be seen that the propagation matrix calculation results of the single-pass and double-pass experiments show a high degree of consistency. The RMSE of each element of the rain area propagation matrix are 0.0206, 0.0273, 0.0025, and 0.0387, respectively, indicating that the inversion method described in this invention can effectively invert actual rainfall propagation information.

[0045] In one embodiment, a library-by-library inversion device for the propagation matrix of rain-medium polarized electromagnetic waves is provided, comprising: The first module is used to obtain the relationship between the electromagnetic waves emitted by the dual polarization antenna and the induced voltage when the transmission medium is rain. The second module is used to uniformly divide the rain zone along the electromagnetic wave propagation path according to the distance from the target area. N layer; The third module is used to construct the observation equations for each rain zone based on the relationship between the electromagnetic waves transmitted by the dual-polarization antenna and the induced voltage, and to obtain the covariance matrix of the observation equations; the observation equations are then transformed into standard form through matrix straightening transformation to obtain the observation matrix; The fourth module is used to construct the state equations for each rain zone and obtain the covariance matrix and state transition matrix of the state equations. The fifth module is used to obtain the linear state vector of the rain region in the input layer based on the state equation; The sixth module is used to obtain the predicted state and prediction error covariance based on the linear state vector of the rain area in the input layer, and to calculate the Kalman gain; the predicted state and prediction error covariance are updated according to the Kalman gain until all rain areas are traversed to obtain the optimal estimated propagation matrix for each rain area; The seventh module is used to calculate the product of the propagation matrices of each rain zone based on the optimal estimated propagation matrix of each rain zone, obtain the optimal estimated propagation matrix of the rain zone, and output it.

[0046] Matters not covered in this invention are common knowledge.

[0047] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A rain medium polarized electromagnetic wave propagation matrix bin-by-bin inversion method, characterized in that, Includes the following steps: When the transmission medium is rain, the relationship between the electromagnetic waves emitted by the dual polarization antenna and the induced voltage is obtained. The rain area on the electromagnetic wave propagation path is divided into N layers; The observation equations for each rain region are constructed based on the relationship between the electromagnetic waves transmitted by the dual-polarization antenna and the induced voltage, and the covariance matrix of the observation equations is obtained. The observation equations are then transformed into standard form through matrix straightening transformation to obtain the observation matrix. Construct the state equations for each rain zone, and obtain the covariance matrix and state transition matrix of the state equations; The linear state vector of the rain region in the input layer is obtained based on the state equation; The predicted state and prediction error covariance are obtained based on the linear state vector of the rain region in the input layer, and the Kalman gain is calculated. The predicted state and prediction error covariance are updated according to the Kalman gain until all rain regions are traversed to obtain the optimal estimated propagation matrix for each rain region. Calculate the product of the propagation matrices of each rain zone based on the optimal estimated propagation matrix, obtain the optimal estimated propagation matrix of the rain zone, and output it.

2. The rain medium polarized electromagnetic wave propagation matrix per bin inversion method of claim 1, wherein, The relationship between the electromagnetic wave emitted by the dual-polarization antenna and the induced voltage is expressed by the following formula: wherein is the induced voltage for the receive antenna horizontal polarization; is the induced voltage for the receive antenna vertical polarization; is the horizontal polarized electromagnetic wave transmitted by the dual polarized antenna; is the vertical polarized electromagnetic wave transmitted by the dual polarized antenna; is the propagation matrix for a rain region on the electromagnetic wave propagation path; is the electromagnetic wave wavelength; is the antenna gain; is the target to radar distance; is the target polarization scattering matrix.

3. The rain medium polarized electromagnetic wave propagation matrix per bin inversion method of claim 2, wherein, The observation equation is expressed by the following formula: wherein, is the first k layer rain cell's distance to the radar; is the first k layer rain cell's linear state vector; is the first k layer rain cell's propagation matrix; is the first k layer rain cell's observation noise.

4. The rain medium polarized electromagnetic wave propagation matrix per bin inversion method of claim 3, wherein, The observation matrix is ​​obtained by following these steps: The observation equation is transformed into its standard form using matrix straightening transformation: wherein is the forward k -1 layer rain region propagation matrix; is the Kronecker product; Obtaining a first k Observation matrix for a layer rain area : 。 5. The rain medium polarized electromagnetic wave propagation matrix library-by- library inversion method of claim 4, wherein, The state equation is expressed by the following formula: wherein is the state transition matrix; is the state vector of the first k layer rain cell; is the state vector of the first k -1 layer rain cell.

6. The rain medium polarized electromagnetic wave propagation matrix per bin inversion method of claim 5, wherein, The predicted state is obtained according to the following formula: wherein, is the k forecast value of the is the k estimated value of the -1th The prediction error covariance is obtained according to the following formula: wherein, is the prior covariance error for the first k layer rain region; is the posterior covariance error for the first k layer rain region; is the prior covariance error for the first k layer rain region; and Q1is the covariance matrix of the process noise for the first layer rain region. The Kalman gain is obtained according to the following formula: wherein, is the Kalman gain for the k layer rain region; is the Kalman gain for the k covariance matrix of the observation noise for the layer rain region.

7. The rain medium polarized electromagnetic wave propagation matrix per bin inversion method of claim 6, wherein, Update the predicted state and prediction error covariance according to the following formula: wherein is the first k layer rain area status estimate; is the updated error covariance.

8. The rain medium polarized electromagnetic wave propagation matrix per bin inversion method of claim 1, wherein, The propagation matrix of each rain zone was calculated using the Oguchi method.

9. The rain medium polarized electromagnetic wave propagation matrix per bin inversion method of claim 1, wherein, The product of the rain propagation matrix is ​​expressed as: wherein is the product of the rain region propagation matrices; is the first N layer rain region optimal estimation propagation matrix.

10. A rain medium polarized electromagnetic wave propagation matrix bin-by-bin inversion device, characterized by, include: The first module is used to obtain the relationship between the electromagnetic waves emitted by the dual polarization antenna and the induced voltage when the transmission medium is rain. The second module is configured to divide rain areas on the electromagnetic wave propagation path into a plurality of distance bins according to distance. N layer; The third module is used to construct the observation equations for each rain zone based on the relationship between the electromagnetic waves transmitted by the dual-polarization antenna and the induced voltage, and to obtain the covariance matrix of the observation equations; the observation equations are then transformed into standard form through matrix straightening transformation to obtain the observation matrix; The fourth module is used to construct the state equations for each rain zone and obtain the covariance matrix and state transition matrix of the state equations. The fifth module is used to obtain the linear state vector of the rain region in the input layer based on the state equation; The sixth module is used to obtain the predicted state and prediction error covariance based on the linear state vector of the rain area in the input layer, and to calculate the Kalman gain; the predicted state and prediction error covariance are updated according to the Kalman gain until all rain areas are traversed to obtain the optimal estimated propagation matrix for each rain area; The seventh module is used to calculate the product of the propagation matrices of each rain zone based on the optimal estimated propagation matrix of each rain zone, obtain the optimal estimated propagation matrix of the rain zone, and output it.

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