X-band dual-polarization phased array rainfall radar system and detection method

Abstract

The present invention relates to the field of meteorological radar technology, and specifically to an X-band dual-polarization phased array rain radar system and detection method. The system includes hardware equipment, a processing module, and auxiliary accessories; the hardware equipment includes an antenna array subsystem and a radar base subsystem; the processing module includes a polarization channel consistency calibration submodule, a particle phase identification submodule, and a rainfall estimation submodule; the auxiliary accessories include power supply equipment, a generator, lightning protection facilities, an optical fiber network, and a data storage device. A planar microstrip patch array antenna is used, which is designed with a spacing arrangement and has the characteristics of small area and high phase center consistency, ensuring high consistency, low side lobes, and high cross-polarization isolation of dual-polarization beams; at the same time, the detection accuracy is improved by setting a processing module.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological radars, and in particular to an X-band dual-polarization phased array rain measurement radar system and a detection method. Background Art

[0002] Dual-polarization phased array radar is a radar system that combines polarization measurement technology and phased array technology. It can not only perform the functions of traditional phased array radar, such as fast and flexible beam pointing and tracking multiple targets, but also provide more information about target characteristics through the measurement of polarization information. Dual-polarization phased array radar can obtain the characteristics of the target's reflected signal in different polarization states, so as to more accurately identify and classify the target, which is especially useful for water vapor particle analysis in meteorological monitoring.

[0003] The Chinese patent with publication number CN113933844A discloses a phased array multi-band integrated transceiver radar and radar detection method, including a wave control unit and a phased array antenna connected to the wave control unit; the phased array antenna includes a first transceiver component, a second transceiver component and a third receiving component, the first transceiver component is used to transmit multiple first beams to multiple different directions of the sky based on the elevation azimuth information, and receive the first information corresponding to the first beam, the second transceiver component is used to transmit multiple second beams to multiple different directions of the sky based on the elevation azimuth information, receive the second information corresponding to the second beam, the third receiving component is used to receive the third information corresponding to the third beam, the wave control unit is used to determine the elevation azimuth information, and is also used to determine the detection information based on the first information, the second information and the third information. There is no need to adjust the transmission direction of the detection radar, and the detection data of multiple satellites can be obtained at the same time, and the cloud and rain distribution information within a certain range can be detected, reducing the complexity of the operation.

[0004] The Chinese patent with the publication number CN111983617A discloses a dual-polarization phased array weather radar, which is composed of a waveguide crack plane antenna array formed by alternating horizontal polarization antennas and vertical polarization antennas; a DTRU subsystem composed of a DTRU module and a heat dissipation fan; a digital beam forming / signal processing subsystem composed of a signal processing and optical transmission and timing synchronization module; and a servo subsystem, a frequency source and distribution network subsystem, and a monitoring subsystem, which can realize the requirements of multiple working modes of the whole system, such as dual-transmit dual-receive, single-transmit dual-receive, and single-transmit single-receive. It can realize the working requirements of pitch-dimensional phase scanning and single / multi-beam reception, quickly detect weather target information, and effectively monitor the occurrence and development of disastrous weather such as heavy rain, hail, and tornadoes; at the same time, dual polarization has a good performance of quantitatively measuring echo intensity, and can quantitatively estimate large-scale precipitation; it realizes the fixed-point, quantitative, and timing precipitation forecast, and provides reliable and scientific detection data for meteorological support.

[0005] However, the above prior art still has the following problems:

[0006] (1) High-precision precipitation estimation depends on the accuracy of dual-polarization measurement of radar, which requires high consistency of equivalent radiated power, beam pointing, beam width, and sidelobe level of dual-polarization beams. The consistency of polarization channels has not been calibrated, and the measurement accuracy needs to be improved.

[0007] (2) The characteristics of particles in different phases are not considered, and the influence of different particle phases on precipitation estimation cannot be effectively eliminated;

[0008] (3) The matching of the landing area is not considered, which results in poor results for refined, near-ground, and precise surface rainfall monitoring. Summary of the invention

[0009] To achieve the purpose of the present invention, the present invention implements an X-band dual-polarization phased array rain detection radar system through the following technical solutions, including hardware equipment, processing modules, and auxiliary supporting equipment;

[0010] The hardware equipment includes an antenna array subsystem and a radar base subsystem;

[0011] The processing module includes a polarization channel consistency calibration submodule, a particle phase identification submodule, and a rainfall estimation submodule;

[0012] The ancillary facilities include power supply equipment, generators, lightning protection facilities, optical fiber networks, and data storage devices.

[0013] Furthermore, the antenna array subsystem includes a dual-polarization array antenna, a radio frequency transceiver unit, a power synthesis and distribution network, an up and down conversion unit, and a digital intermediate frequency processing unit; the radar base subsystem includes a beam synthesis control unit, a system monitoring unit, a signal processing unit, a main power supply, and a servo control unit.

[0014] Furthermore, the dual-polarized array antenna adopts a planar microstrip patch array antenna;

[0015] The dual-polarized array antenna array adopts a rectangular aperture, and the elevation and azimuth are separable. There are 64 rows of dual-polarized microstrip patch linear arrays in the elevation direction, and each row of linear arrays in the azimuth direction has 32 radiating units; the feeding interface of each subarray of the antenna is located on the back of the radiating surface.

[0016] Furthermore, the radiation units are respectively rectangular radiation patches, horizontal and vertical coupling slots, and feeding transmission lines from the upper layer to the lower layer.

[0017] The algorithm of the polarization channel consistency calibration submodule is as follows:

[0018] The equivalent conversion between aperture field and far field is adopted. After measuring the amplitude and phase of the aperture field, the far field conversion is performed to obtain the far field directivity diagram.

[0019] Based on the objective function, the particle swarm population is initialized using logistic mapping, and the optimized far-field pattern is obtained through the particle swarm algorithm.

[0020] The classification algorithm of the particle phase identification submodule includes melt layer identification, data confidence calculation and membership function calculation, and the specific steps are as follows:

[0021] (1) Identify the melting layer based on the quasi-vertical profile technology to obtain the melting layer identification information and precipitation type identification information;

[0022] (2) Establish membership functions by combining the scattering characteristics of hydrometeors with the identification information of melt layers and precipitation types;

[0023] (3) Optimize the membership function using K-means clustering analysis algorithm;

[0024] (4) The information of each polarization parameter is integrated using fuzzy logic methods to identify and classify multiple particle phases.

[0025] The rainfall estimation submodule includes a rainfall area correction model and a random forest model. The training steps of the random forest model are as follows:

[0026] (1) Obtain automatic station rainfall data, altitude and longitude and latitude of radar grid points, and radar polarization parameters, including radar horizontal reflectivity factor, differential reflectivity factor, and differential propagation phase shift;

[0027] (2) The automatic station rainfall corresponding to the radar grid point is used as the output value, and other data is used as the input value to train the initial model;

[0028] (3) The rainfall product grid data are constrained according to the probability density distribution of the intensity grid data to generate a random forest model.

[0029] The present invention also includes an X-band dual-polarization phased array rain detection radar detection method, comprising the following steps:

[0030] S1, radar parameter setting, the user terminal sends the parameters to the system monitoring unit through the network, and the system monitoring unit sends the parameters to the signal processing unit and the beamforming control unit respectively;

[0031] S2, the beam synthesis control unit calculates the beam direction data and sends it to the digital intermediate frequency processing unit and the radio frequency transceiver unit;

[0032] S3, dual-polarized array antenna completes signal transmission and reception;

[0033] S4. The processing module completes data calculation through the channel consistency calibration submodule, the particle phase identification submodule, and the rainfall estimation submodule, and sends it to the remote user terminal through the network.

[0034] The beneficial effects of the present invention are:

[0035] 1. Set up the polarization channel consistency calibration submodule, establish the mapping function between the far-field pattern distribution difference and the channel amplitude phase, combine the Logistic mapping to assign the initial value, and obtain the optimized far-field pattern based on the particle swarm algorithm, which is more accurate to approach the target pattern; while maintaining the dual-polarization beam equivalent radiated power, beam pointing, beam width, and sidelobe electrical consistency, the pattern peak can be increased, and the stability of the beam can be improved, which effectively solves the problem of radar dual-polarization detection accuracy;

[0036] 2. Set up a particle phase recognition submodule, and use the characteristic performance of particles of different phases on different radar polarization quantities to establish a recognition algorithm based on fuzzy logic, so as to realize the automatic recognition of different particle phases such as light rain, moderate rain, heavy rain, large drops, hail, sleet / hail, snow, wet snow, ice crystals, and ground clutter, effectively eliminate the influence of different particle phases on precipitation estimation, and improve the accuracy of precipitation estimation;

[0037] 3. Set up the rainfall estimation submodule, correct the error of rainfall amount through the falling area correction model, use the random forest model to replace the static rainfall relationship and constrain the grid data of rainfall products, so that the correlation coefficient is improved, the root mean square error is reduced, and the measurement accuracy is significantly improved;

[0038] 4. The planar microstrip patch array antenna is designed with a spacing arrangement, which has the characteristics of small area and high phase center consistency, ensuring high consistency, low sidelobe and high cross-polarization isolation of the dual-polarization beam. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the structure of an X-band dual-polarization phased array rain detection radar system of the present invention; DETAILED DESCRIPTION

[0040] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0041] Embodiment 1

[0042] according to Figure 1 As shown, this embodiment provides an X-band dual-polarization phased array rain detection radar system, including hardware equipment, processing modules, and auxiliary supporting equipment;

[0043] The hardware equipment includes an antenna array subsystem and a radar base subsystem;

[0044] The processing module includes a polarization channel consistency calibration submodule, a particle phase identification submodule, and a rainfall estimation submodule;

[0045] The ancillary facilities include power supply equipment, generators, lightning protection facilities, optical fiber networks, and data storage devices.

[0046] More specifically, the antenna array subsystem includes a dual-polarized array antenna, a radio frequency transceiver unit, a power synthesis and distribution network, an up and down conversion unit, and a digital intermediate frequency processing unit; the radar base subsystem includes a beam synthesis control unit, a system monitoring unit, a signal processing unit, a main power supply, and a servo control unit.

[0047] More specifically, the dual-polarized array antenna adopts a planar microstrip patch array antenna;

[0048] The dual-polarized array antenna array adopts a rectangular aperture, and the elevation and azimuth are separable. There are 64 rows of dual-polarized microstrip patch linear arrays in the elevation direction, and each row of linear arrays in the azimuth direction has 32 radiating units; the feeding interface of each subarray of the antenna is located on the back of the radiating surface.

[0049] More specifically, the radiation units are respectively a rectangular radiation patch, a horizontal and vertical coupling slots, and a feeding transmission line from the upper layer to the lower layer.

[0050] More specifically, the radiation unit spacing satisfies:

[0051]

[0052] Where λ is the wavelength of the antenna operating frequency, θ is the scanning angle, and Δθ is half the beam width.

[0053] More specifically, the algorithm of the polarization channel consistency calibration submodule is as follows:

[0054] The equivalent conversion between aperture field and far field is adopted. After measuring the amplitude and phase of the aperture field, the far field conversion is performed to obtain the far field pattern. The far field pattern of each polarization is expressed as:

[0055]

[0056] in, u=kdcos(θ),θ=0~180°, The number of radar units is N, the spacing is d, and the measured amplitude and phase distribution of each channel are I n and α n ;

[0057] Given a directivity pattern f(θ), by changing the amplitude I of the array element while keeping the number of array elements N, the unit spacing d and the wavelength λ unchanged n and phase α n Distribution, so that it forms the same direction pattern as f(θ);

[0058] Sample the given directional pattern, set the number of sampling points to M, and construct the objective function:

[0059]

[0060] Among them, S max =max(S(I,α,θ));

[0061] Based on the objective function, the particle swarm population is initialized using logistics mapping, and the optimized far-field pattern is obtained through the particle swarm algorithm. Combined with the vectorized objective function, the particle swarm algorithm can effectively increase the convergence efficiency of the algorithm, making the optimized pattern more accurately approach the target pattern, and by adding constraints, the result can better meet expectations.

[0062] More specifically, the classification algorithm of the particle phase identification submodule includes melt layer identification, data confidence calculation and membership function calculation, and the specific steps are as follows:

[0063] (1) Identify the melting layer based on the quasi-vertical profile technology to obtain the melting layer identification information and precipitation type identification information;

[0064] (2) Establish membership functions by combining the scattering characteristics of hydrometeors with the identification information of melt layers and precipitation types;

[0065] (3) Optimize the membership function using K-means clustering analysis algorithm;

[0066] (4) The information of each polarization parameter is integrated using fuzzy logic methods to identify and classify multiple particle phases.

[0067] Before identification, the original radar observation data needs to be subjected to quality control such as noise removal, attenuation correction, and KDP segmented least squares fitting to improve the reliability of dual-polarization observations.

[0068] The X-band dual-polarization phased array rain radar has a horizontal resolution of 30m and has the characteristics of high temporal and spatial resolution and easy network deployment. The number of elevation angles of the X-band dual-polarization phased array rain radar ranges from 35 to 40 layers, which can provide more intensive observation data in the vertical direction and has greater advantages in particle phase identification.

[0069] More specifically, the rainfall estimation submodule includes a rainfall area correction model and a random forest model, and the training steps of the random forest model are as follows:

[0070] (1) Obtain automatic station rainfall data, altitude and longitude and latitude of radar grid points, and radar polarization parameters, including radar horizontal reflectivity factor, differential reflectivity factor, and differential propagation phase shift;

[0071] (2) The automatic station rainfall corresponding to the radar grid point is used as the output value, and other data is used as the input value to train the initial model;

[0072] (3) The rainfall product grid data are constrained according to the probability density distribution of the intensity grid data to generate a random forest model.

[0073] More specifically, the calculation steps of the landing zone correction model are as follows:

[0074] (1) Calculate the falling speed of raindrops. The lowest elevation angle of the radar is usually within the observation range of the boundary layer. The movement of raindrops can be regarded as the movement of falling points within the boundary layer. The motion equation of raindrops within this range takes into account the gravity, buoyancy and drag (resistance) of the raindrops, which can be expressed as:

[0075]

[0076]

[0077] f(N Re )=a1N Re b1 +a2N Re b2

[0078] in, represents the gravity on the raindrop, and represents the buoyancy and resistance of the raindrop, S represents the area that the water drop passes through, represents the falling speed of raindrops, represents the speed of the ambient air, C d Indicates the drag coefficient, N Re represents the Reynolds number, r represents the radius of the water droplet, ρ a represents the air density, ρ w represents the raindrop density, η represents the air viscosity; in addition, the coefficients a1 = 0.17, a2 = 1.0 × 10 -6 , b1=0.632, b2=2.25;

[0079] Combining the above formulas, we get:

[0080]

[0081] in, represents the falling speed of raindrops, Represents the speed of the ambient air, N Re represents the Reynolds number, r represents the radius of the water droplet, ρ a represents the air density, ρ w represents the density of raindrops, and η represents the viscosity of air;

[0082] (2) Calculate the raindrop falling time based on the raindrop falling speed and the radar grid point height above the ground;

[0083] (3) Combined with the horizontal wind field interpolated by the automatic station, the offset of the falling raindrops is calculated to correct the error between the radar estimated rainfall and the landing rainfall.

[0084] Embodiment 2

[0085] An X-band dual-polarization phased array rain detection radar detection method comprises the following steps:

[0086] S1, radar parameter setting, the user terminal sends the parameters to the system monitoring unit through the network, and the system monitoring unit sends the parameters to the signal processing unit and the beamforming control unit respectively;

[0087] S2, the beam synthesis control unit calculates the beam direction data and sends it to the digital intermediate frequency processing unit and the radio frequency transceiver unit;

[0088] S3, dual-polarized array antenna completes signal transmission and reception;

[0089] S4. The processing module completes data calculation through the channel consistency calibration submodule, the particle phase identification submodule, and the rainfall estimation submodule, and sends it to the remote user terminal through the network.

[0090] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An X-band dual-polarization phased array rain radar system, including hardware equipment, processing modules, and auxiliary equipment, characterized in that: The hardware equipment includes an antenna array subsystem and a radar base subsystem; The processing module includes a polarization channel consistency calibration submodule, a particle phase identification submodule, and a rainfall estimation submodule; The algorithm of the polarization channel consistency calibration submodule includes: adopting equivalent conversion between aperture field and far field, measuring the amplitude and phase of the aperture field, performing far field conversion to obtain a far field pattern, initializing a particle swarm population based on an objective function using logistics mapping, and obtaining an optimized far field pattern through a particle swarm algorithm; The classification algorithm of the particle phase state identification submodule includes melt layer identification, data confidence calculation and membership function calculation, and the specific steps include: melt layer identification based on quasi-vertical profile technology, obtaining melt layer identification information and precipitation type identification information, establishing a membership function by combining the melt layer identification information and precipitation type identification information with the hydrometeor scattering characteristics, optimizing the membership function by using the K-means clustering analysis algorithm, integrating the information of each polarization parameter by using the fuzzy logic method, and performing multiple particle phase state identification and classification; The rainfall estimation submodule includes a rainfall area correction model and a random forest model. The random forest model training steps include: obtaining automatic station rainfall data, the altitude and longitude and latitude of radar grid points, and radar polarization parameters, including radar degree reflectivity factor, differential reflectivity factor, and differential propagation phase shift, using the automatic station rainfall corresponding to the radar grid point as the output value and other data as the input value, training the initial model, constraining the rainfall product grid point data according to the probability density distribution of the intensity grid point data, and generating a random forest model; The ancillary facilities include power supply equipment, generators, lightning protection facilities, optical fiber networks, and data storage devices.

2. The X-band dual-polarization phased array rainfall radar system according to claim 1, characterized in that: The antenna array subsystem includes a dual-polarized array antenna, a radio frequency transceiver unit, a power synthesis and distribution network, an up-down frequency conversion unit and a digital intermediate frequency processing unit; The radar base subsystem includes a beamforming control unit, a system monitoring unit, a signal processing unit, a main power supply, and a servo control unit.

3. The X-band dual-polarization phased array rainfall radar system according to claim 2, characterized in that: The dual-polarized array antenna adopts a planar microstrip patch array antenna; The dual-polarized array antenna array adopts a rectangular aperture, and the elevation and azimuth are separable. There are 64 rows of dual-polarized microstrip patch linear arrays in the elevation direction, and each row of linear arrays in the azimuth direction has 32 radiating units; the feeding interface of each subarray of the antenna is located on the back of the radiating surface.

4. The X-band dual-polarization phased array rainfall radar system according to claim 3, characterized in that: The radiation units are respectively rectangular radiation patches, horizontal and vertical coupling slots, and feeding transmission lines from the upper layer to the lower layer.

5. An X-band dual-polarization phased array rain radar detection method, applied to an X-band dual-polarization phased array rain radar system as claimed in any one of claims 1 to 4, characterized in that: The following steps are involved: S1, radar parameter setting, the user terminal sends the parameters to the system monitoring unit through the network, and the system monitoring unit sends the parameters to the signal processing unit and the beamforming control unit respectively; S2, the beam synthesis control unit calculates the beam direction data and sends it to the digital intermediate frequency processing unit and the radio frequency transceiver unit; S3, dual-polarized array antenna completes signal transmission and reception; S4. The processing module completes data calculation through the channel consistency calibration submodule, the particle phase identification submodule, and the rainfall estimation submodule, and sends it to the remote user terminal through the network.

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