A quantitative method for generating simulated target reflectivity factors for weather radar far-field calibration

Abstract

The embodiments of the present disclosure provide a method for quantitatively generating a simulated target reflectivity factor for calibration of a weather radar in the far field, which is applied to the field of weather radar calibration technology. The method includes obtaining radar antenna and emission parameters, the distance between the target simulator installation point and the radar, the simulated target distance, and the transmit-receive ratio of the target simulator; according to a pre-constructed simulated target equivalent cross-sectional area quantitative model, based on the radar antenna and emission parameters, the distance between the target simulator installation point and the radar, the transmit-receive ratio of the target simulator and the simulated target distance, the simulated target equivalent cross-sectional area is calculated; according to the simulated target equivalent cross-sectional area, the radar antenna and emission parameters, and the weather radar equation, the expected value of the radar reflectivity factor is calculated. In this way, the simulated target reflectivity factor for calibration of the weather radar in the far field can be quantitatively generated.

Description

Technical Field

[0001] The present disclosure relates to the technical field of weather radar calibration, and in particular to a method for quantitatively generating a simulated target reflectivity factor for far-field calibration of a weather radar. Background Art

[0002] The significance of radar automatic calibration is to improve the measurement accuracy and reliability of the radar system, ensuring that the radar can accurately detect and measure targets, thereby improving the accuracy and safety of decision-making.

[0003] Radar automatic calibration verifies and adjusts radar system parameters by using known calibration targets to ensure that the radar can accurately measure precipitation or other meteorological targets in the atmosphere. This calibration process is crucial to improving the accuracy of weather forecasts, as accurate weather radar data is one of the key factors in weather forecasts. Through calibration, it can ensure that the weather radar system accurately measures precipitation data and improves the accuracy of quantitative precipitation estimates, which provides important information for water resource management, flood warning, agriculture and other fields to support relevant decisions and actions.

[0004] For weather radar calibration, it is particularly important to quantify the equivalent cross-sectional area of ​​the simulated target and generate the expected radar reflectivity factor to facilitate the staff to carry out subsequent radar calibration work. Summary of the invention

[0005] The present invention provides a method for quantitatively generating a simulated target reflectivity factor for far-field calibration of a weather radar.

[0006] According to a first aspect of the present disclosure, a method for quantitatively generating a simulated target reflectivity factor for calibration of a weather radar in the far field is provided. The method comprises:

[0007] Obtain radar antenna and transmission parameters, the distance between the target simulator installation point and the radar, the simulated target distance, and the target simulator's transmit-receive radiation ratio;

[0008] The simulated target equivalent cross-sectional area is calculated based on the pre-constructed simulated target equivalent cross-sectional area quantitative model, the radar antenna and emission parameters, the distance between the target simulator installation point and the radar, the transmit-receive radiation ratio of the target simulator and the simulated target distance;

[0009] The expected value of the radar reflectivity factor is calculated based on the simulated target equivalent cross-sectional area, radar antenna and transmission parameters, and weather radar equation.

[0010] According to the above aspects and any possible implementation, an implementation is further provided.

[0011] The radars include S-band, C-band and X-band weather radars;

[0012] The transmit-receive radiation ratio of the target simulator is obtained by the following steps:

[0013] Obtaining a test real amplitude ratio of a target simulator; the test real amplitude ratio is obtained by calibrating the target simulator with a spectrum analyzer and a standard signal source;

[0014] The difference between the actual amplitude ratio of the test and the amplitude ratio set by the target simulator is calculated as the transmit-receive amplitude ratio of the target simulator.

[0015] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the radar antenna and the transmission parameters include:

[0016] Target simulator antenna gain, radar operating wavelength.

[0017] According to the above aspects and any possible implementation, an implementation is further provided.

[0018] The quantitative model of the simulated target equivalent cross-sectional area is:

[0019] ,

[0020] in, To simulate the target equivalent cross-sectional area, is the target simulator receiving antenna gain, is the target simulator transmit antenna gain, is the transmit-receive ratio of the target simulator, The distance between the target simulator setting point and the radar, Simulate target distances for target simulators, The operating wavelength of the radar.

[0021] According to the above aspects and any possible implementation, an implementation is further provided, wherein the simulated target equivalent cross-sectional area quantitative model is constructed by the following steps:

[0022] According to the radar working wavelength and the target simulator receiving antenna gain, a target simulator receiving antenna aperture area calculation model is constructed;

[0023] According to the radar transmitting power, radar transmitting antenna gain and the distance between the target simulator and the radar, a radar radiation power density calculation model for the target simulator installation point is constructed;

[0024] According to the target simulator receiving antenna aperture area calculation model and the target simulator installation point radar radiation power density calculation model, the target simulator antenna received radar radiation power calculation model is constructed;

[0025] According to the radar radiation power calculation model received by the target simulator antenna, the transmit-receive radiation ratio of the target simulator and the simulated target transmitting antenna gain generated by the target simulator, a simulation model of the equivalent radiation power of the simulated target generated by the target simulator is constructed;

[0026] According to the calculation model of the simulated target equivalent radiation power generated by the target simulator and the weather radar equation, a quantitative model of the simulated target equivalent cross-sectional area is constructed.

[0027] According to the above aspects and any possible implementation, an implementation is further provided.

[0028] The method of constructing a simulated target equivalent cross-sectional area quantitative model based on the simulated target equivalent radiation power calculation model generated by the target simulator and the weather radar equation includes:

[0029] According to the calculation model of the simulated target equivalent radiation power generated by the target simulator and the first weather radar equation, the radar receiving power calculation model is obtained;

[0030] According to the radar receiving power calculation model and the second atmospheric radar equation, a quantitative model of the simulated target equivalent cross-sectional area is obtained.

[0031] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method further includes:

[0032] Taking the logarithm of the expected value of the radar reflectivity factor, we can obtain the expected value of the radar echo intensity.

[0033] The radar is calibrated based on the expected value of the radar echo strength.

[0034] According to a second aspect of the present disclosure, a device for quantitatively generating a simulated target reflectivity factor for calibration of a weather radar in the far field is provided. The device comprises:

[0035] The data acquisition module is used to obtain the radar antenna and transmission parameters, the distance between the target simulator installation point and the radar, the simulated target distance, and the transmit-receive radiation ratio of the target simulator;

[0036] A calculation module is used to calculate the simulated target equivalent cross-sectional area according to a pre-constructed simulated target equivalent cross-sectional area quantitative model, based on the radar antenna and transmission parameters, the distance between the target simulator installation point and the radar, the transmit-receive radiation ratio of the target simulator and the simulated target distance;

[0037] The calculation module is also used to calculate the expected value of the radar reflectivity factor according to the equivalent cross-sectional area of ​​the simulated target, the radar antenna and transmission parameters, and the weather radar equation.

[0038] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0039] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect of the present disclosure is implemented.

[0040] The embodiment of the present disclosure provides a method for quantitatively generating a simulated target reflectivity factor for calibration of a weather radar in the far field. By designing a quantitative calculation model for the simulated target equivalent cross-sectional area, the simulated target equivalent cross-sectional area can be quickly calculated, and the corresponding radar reflectivity factor expected value can be quantitatively generated, so that the staff can calibrate the radar according to the radar reflectivity factor expected value. The whole process is simple and efficient to operate, does not require the coordination between multiple devices, has low requirements for the equipment, and greatly reduces the difficulty of radar calibration while ensuring accuracy.

[0041] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0043] Figure 1 A flow chart showing a method for quantitatively generating a simulated target reflectivity factor for far-field calibration of a weather radar according to an embodiment of the present disclosure is shown;

[0044] Figure 2 A block diagram of a device for quantitatively generating a simulated target reflectivity factor for far-field calibration of a weather radar according to an embodiment of the present disclosure is shown;

[0045] Figure 3 A schematic block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown;

[0046] Figure 4 A schematic diagram of a first test connection mode for performing a calibration test on a target simulator by using a spectrum analyzer and a standard signal source, which can implement an embodiment of the present disclosure, is shown;

[0047] Figure 5A schematic diagram of a second test connection mode for performing a calibration test on a target simulator by using a spectrum analyzer and a standard signal source, which can implement an embodiment of the present disclosure, is shown;

[0048] Figure 6 A schematic diagram showing the relationship between a target simulator capable of implementing an embodiment of the present disclosure and a radar when the target simulator is working normally is shown. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0050] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0051] Figure 1 A flowchart of a method 100 for quantitatively generating a simulated target reflectivity factor for far-field calibration of a weather radar according to an embodiment of the present disclosure is shown. The method 100 includes:

[0052] Step 110, obtaining radar antenna and transmission parameters, distance data between the target simulator installation point and the radar, simulated target distance, and the transmit-receive radiation ratio of the target simulator.

[0053] In some embodiments, the radar includes a spectrum analyzer and a standard signal source; the transmit-receive amplitude ratio of the target simulator is obtained through the following steps: obtaining the test real amplitude ratio of the target simulator; the test real amplitude ratio is obtained by calibrating the target simulator with a spectrum analyzer and a standard signal source; and calculating the difference between the test real amplitude ratio and the target simulator set amplitude ratio as the transmit-receive amplitude ratio of the target simulator.

[0054] For example, Figure 4 The first test connection method diagram shown in the figure is to calibrate the target simulator through the spectrum analyzer and the standard signal source. First, connect the two RF cables to the signal source and the spectrum analyzer respectively, and then connect the other ends of the two RF cables with double female connectors. Use the signal source to generate a 0dBm (unit of power) signal, and test it through the spectrum analyzer. The test result is recorded as P1 (signal power). Figure 5The second test connection method schematic diagram of the target simulator calibration test through the spectrum analyzer and the standard signal source is shown in the figure. The setting parameters of the signal source and the spectrum analyzer remain unchanged. The target simulator generates a simulated target (the amplitude ratio is 0). Figure 5 The test result is recorded as P2 (signal power). When the target simulator receiving test point works normally, the horizontal receiving antenna is connected; when the target simulator transmitting test point works normally, the transmitting antenna is connected. After the above two tests, the actual amplitude ratio of the target simulator can be obtained as:

[0055] Δbm = P2-P1+0.1,

[0056] The 0.1 here means that the second test requires one more RF double female connector than the first test (loss difference 0.1dB). The difference between the obtained Δbm (real amplitude ratio) and the current target simulator setting amplitude ratio (for example, in order to achieve the best effect, artificially defined as: 0dB) is the correction amount required for the target simulator (that is, the transmit-receive amplitude ratio of the target simulator). The amplitude ratio correction can be completed by putting this correction amount into the target simulator amplitude ratio correction configuration parameters.

[0057] The above process is described in detail below with reference to a set of data.

[0058] During the test, the test result of P1 was -4.1dB. The values ​​of P2 for the four simulated targets were all 4.5dB. Through the above two test results, it can be seen that the actual amplitude ratio of the target simulator is the second test result minus the first test result plus the insertion loss of a double female connector. That is: -4.55-(-4.1)+0.1=-0.35dB. Therefore, the amplitude ratio during the test is set to 0, but the actual amplitude ratio is -0.35, so the setting value of the four simulated targets during the test should be subtracted by 0.35. The purpose of this design is: for example, the target simulator's transmitting antenna gain is 8.91dB, and the receiving antenna gain is 8.27dB. The gain of the transmitting antenna is 0.64dB greater than the gain of the receiving antenna. Therefore, this 0.64dB needs to be corrected in the amplitude ratio. That is, when the target simulator sets the amplitude ratio to 0, the transmitting signal is 0.64dB smaller than the receiving signal. This can make the spatial energy density radiated by the target simulator through the antenna equal to the spatial energy density received by the radar (that is, the target simulator is an ideal reflector). Then in actual calculation, the theoretical value of 0dB cannot be used in the radar echo intensity test value calculation model, so the setting value at that time needs to be corrected. The following is the data comparison result before and after the correction of the amplitude ratio of the test simulation of 4 targets.

[0059] Table 1: Target simulator test four simulation target setting parameters (before correction)

[0060]

[0061] Table 2: Target simulator test four simulation target setting parameters (after correction)

[0062]

[0063] In some embodiments, the radar antenna and transmission parameters include: target simulator antenna gain, radar operating wavelength.

[0064] In some embodiments, in order to substitute the pre-built simulated target equivalent cross-sectional area quantitative model and quickly obtain the simulated target equivalent cross-sectional area, it is necessary to obtain the radar antenna and transmission parameters, the distance data between the target simulator installation point and the radar, the target simulator's transmit-receive ratio and the simulated target distance. The target simulator may be a calibrator, which generates the simulated target. Figure 6 The diagram shown is a schematic diagram of the setup relationship between a set of target simulators and a radar when working normally.

[0065] Step 120, according to the pre-constructed quantitative model of the equivalent cross-sectional area of ​​the simulated target, based on the radar antenna and transmission parameters, the distance between the target simulator installation point and the radar, the transmit-receive radiation ratio of the target simulator and the simulated target distance, the equivalent cross-sectional area of ​​the simulated target is calculated.

[0066] In some embodiments, the simulated target equivalent cross-sectional area quantitative model is constructed by the following steps: constructing a target simulator receiving antenna aperture area calculation model based on the radar operating wavelength and the target simulator receiving antenna gain; constructing a target simulator installation point radar radiation power density calculation model based on the radar transmit power, the radar transmit antenna gain and the distance between the target simulator and the radar; constructing a radar radiation power calculation model received by the target simulator antenna based on the target simulator receiving antenna aperture area calculation model and the target simulator installation point radar radiation power density calculation model; constructing a simulated target equivalent radiation power calculation model generated by the target simulator based on the radar radiation power calculation model received by the target simulator antenna, the target simulator's transmit-receive radiation ratio and the simulated target transmitting antenna gain generated by the target simulator; constructing a simulated target equivalent cross-sectional area quantitative model based on the simulated target equivalent radiation power calculation model generated by the target simulator and the weather radar equation.

[0067] In some embodiments, (1) a target simulator receiving antenna aperture area calculation model is constructed based on the radar operating wavelength and the target simulator receiving antenna gain. as follows:

[0068] ,

[0069] in, : Target simulator receiving antenna gain, : Radar operating wavelength;

[0070] (2) Based on the radar transmit power, radar transmit antenna gain, and the distance between the target simulator and the radar, a radar radiation power density calculation model is constructed at the target simulator installation point. as follows:

[0071] ,

[0072] in, : Radar transmitting power, : Radar transmitting antenna gain, : The distance between the target simulator installation point and the radar;

[0073] (3) Based on the target simulator receiving antenna aperture area calculation model and the target simulator installation point radar radiation power density calculation model, a radar radiation power calculation model for the target simulator antenna is constructed. as follows:

[0074] ,

[0075] (4) Based on the calculation model of the radar radiation power received by the target simulator antenna, the transmit-receive ratio of the target simulator, and the simulated target transmitting antenna gain generated by the target simulator, a calculation model of the simulated target equivalent radiation power generated by the target simulator is constructed. as follows:

[0076] ,

[0077] in, : The transmit-receive ratio of the target simulator, G bt : Simulate the target transmitting antenna gain;

[0078] (5) The process of constructing a simulated target equivalent cross-sectional area quantitative model according to the simulated target equivalent radiation power calculation model generated by the target simulator and the weather radar equation includes: obtaining a radar receiving power calculation model according to the simulated target equivalent radiation power calculation model generated by the target simulator and the first weather radar equation; obtaining a simulated target equivalent cross-sectional area quantitative model according to the radar receiving power calculation model and the second weather radar equation, as follows:

[0079] First, according to the simulation target equivalent radiated power calculation model And the first weather radar equation, build a radar receiving power calculation model as follows:

[0080] ,

[0081] Among them, the first weather radar equation is: , G r : Radar receiving antenna gain;

[0082] Secondly, according to the simulated target echo power calculation model With the second atmospheric radar equation, the process of constructing a quantitative model of the equivalent cross-sectional area of ​​a simulated target is as follows:

[0083] ,

[0084] Among them, the second atmospheric radar equation is: , where, To simulate the target equivalent cross-sectional area, is the target simulator receiving antenna gain, is the target simulator transmit antenna gain, is the transmit-receive ratio of the target simulator, The distance between the target simulator setting point and the radar, Simulate target distances for target simulators, is the radar operating wavelength. That is, let the radar receiving power calculation model be equal to the second atmospheric radar equation, and we get the formula: , and then simplify the formula to get the quantitative model of the simulated target equivalent cross-sectional area.

[0085] Step 130, calculating the expected value of the radar reflectivity factor according to the simulated target equivalent cross-sectional area, radar antenna and transmission parameters, and weather radar equation.

[0086] In some embodiments, let the weather radar equation and After the relationship between the radar reflectivity factor and the simulated target equivalent cross-sectional area is transformed, the radar reflectivity factor calculation model is obtained. The simulated target equivalent cross-sectional area, radar antenna and transmission parameters are substituted into the radar reflectivity factor calculation model to obtain the radar reflectivity factor expected value Z. Among them, : Radar antenna gain (dB), : Radar transmit pulse width (μs), : antenna horizontal beam width (°), : antenna vertical beam width (°), : speed of light, : When the particles are in water state, ≈0.93, when the particle is in the icy state, ≈0.20, R: simulated target distance; where radar receiving antenna gain = radar transmitting antenna gain = radar antenna gain, that is, G = G t =G r .

[0087] Based on the above embodiment, the method further includes: taking the logarithm of the expected value of the radar reflectivity factor to obtain the expected value of the radar echo intensity; and performing radar calibration according to the expected value of the radar echo intensity.

[0088] In some embodiments, the radar reflectivity factor calculation model constructed in the above manner is logarithmized to obtain the radar echo intensity expected value calculation model:

[0089] ,

[0090] in, is the target simulator receiving antenna gain, is the target simulator transmit antenna gain, is the transmit-receive ratio of the target simulator, The distance between the target simulator setting point and the radar, Simulate target distances for target simulators, is the radar operating wavelength, is the radar transmit pulse width, is the antenna horizontal beam width, is the vertical beam width of the antenna.

[0091] In some embodiments, for example, the radar transmit pulse width is 1.57 μs, the antenna horizontal beam width is 0.9065°, the antenna vertical beam width is 0.893°, and the transmit frequency is 2845 MHz (calculated according to the working wavelength); the target simulator antenna gain is 8.27 dB (receiving), 8.91 dB (transmitting), the target simulator is set up at a point of 10.14 km, and 6 target distances are simulated. The expected value of the radar echo intensity is shown in Table 3.

[0092] Table 3: S-band weather radar target simulator installation point (10.14km) strength expected results

[0093] Simulated target distance (km) Simulated target amplitude ratio (dB) Intensity (dBz) 18.5 0 42.95 50 0 51.59 60 -3 50.17 70 -6 48.51 80 -9 46.67 18.5 0 42.96

[0094] In some embodiments, a table of correspondence between a set of deviations between expected radar echo intensity values ​​and actual radar echo intensity values ​​and parameter deviations as shown in Table 4 below provides convenience for staff to perform rapid radar calibration.

[0095] Table 4: Correspondence between strength deviation and parameter deviation

[0096] Parameter name Deviation from actual value Strength Deviation Target simulator receiving antenna gain Deviation 0.1dB Deviation 0.1dB Target simulator transmit antenna gain Deviation 0.1dB Deviation 0.1dB Target simulator simulates target amplitude ratio Deviation 0.1dB Deviation 0.1dB Distance between target simulator installation point and radar Deviation 1m Deviation 0.002dB Target distance simulated by target simulator Deviation 1m Deviation 0.0005dB Working wavelength Deviation 1MHz Deviation 0.01dB Radar transmit pulse width Deviation 0.01us Deviation 0.028dB Antenna horizontal beam width Deviation 0.01° Deviation 0.048dB Antenna vertical beam width Deviation 0.01° Deviation 0.048dB

[0097] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0098] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0099] Figure 2 FIG. 2 shows a block diagram of a simulated target reflectivity factor quantitative generation device 200 for far-field calibration of a weather radar according to an embodiment of the present disclosure. Figure 2 As shown, the device 200 includes:

[0100] The data acquisition module 210 is used to obtain the radar antenna and transmission parameters, the distance between the target simulator installation point and the radar, the simulated target distance, and the transmit-receive radiation ratio of the target simulator;

[0101] The calculation module 220 is used to calculate the simulated target equivalent cross-sectional area according to the pre-constructed simulated target equivalent cross-sectional area quantitative model, based on the radar antenna and transmission parameters, the distance between the target simulator installation point and the radar, the transmit-receive radiation ratio of the target simulator and the simulated target distance;

[0102] The calculation module 220 is further used to calculate the expected value of the radar reflectivity factor according to the simulated target equivalent cross-sectional area, radar antenna and transmission parameters, and weather radar equation.

[0103] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0104] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0105] Figure 3A schematic block diagram of an electronic device 300 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0106] The electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a ROM 302 or a computer program loaded from a storage unit 308 into a RAM 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An I / O interface 305 is also connected to the bus 304.

[0107] A number of components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0108] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0109] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0110] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0111] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0112] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0113] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0114] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0115] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0116] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for quantitatively generating a simulated target reflectivity factor for calibration of a weather radar in the far field, characterized in that: include: Obtain radar antenna and transmission parameters, the distance between the target simulator installation point and the radar, the simulated target distance, and the transmit-receive ratio of the target simulator; According to the pre-constructed quantitative model of the equivalent cross-sectional area of ​​the simulated target, the equivalent cross-sectional area of ​​the simulated target is calculated based on the radar antenna and emission parameters, the distance between the target simulator installation point and the radar, the simulated target distance, and the transmit-receive ratio of the target simulator; wherein, The simulated target equivalent cross-sectional area is calculated by the following formula: in, To simulate the target equivalent cross-sectional area, is the target simulator receiving antenna gain, is the target simulator transmit antenna gain, is the transmit-receive ratio of the target simulator, The distance between the target simulator setting point and the radar, Simulate target distances for target simulators, is the radar operating wavelength; The expected value of the radar reflectivity factor is calculated based on the simulated target equivalent cross-sectional area, the radar antenna and transmission parameters, and the preset weather radar equation; wherein, The expected value of the radar reflectivity factor is calculated by the following formula: Where Z is the expected value of the radar reflectivity factor, is the radar transmit pulse width, θ is the antenna horizontal beam width, is the vertical beam width of the antenna, c is the speed of light, and when the particle is in water state, ≈0.93, when the particle is in the icy state, ≈0.20, R is the simulated target distance; where radar receiving antenna gain = radar transmitting antenna gain = radar antenna gain, that is, G=Gt=Gr.

2. The method according to claim 1, characterized in that The radars include S-band, C-band and X-band weather radars; The transmit-receive radiation ratio of the target simulator is obtained by the following steps: Obtain the actual amplitude ratio of the test of the target simulator; The actual amplitude ratio of the test is obtained by calibrating the target simulator with a spectrum analyzer and a standard signal source; The difference between the actual amplitude ratio of the test and the set amplitude ratio of the target simulator is calculated as the transmit-receive amplitude ratio of the target simulator.

3. The method according to claim 1, characterized in that The radar antenna and transmission parameters include: Target simulator antenna gain, radar operating wavelength.

4. The method according to claim 1, characterized in that: The simulated target equivalent cross-sectional area quantitative model is constructed by the following steps: According to the radar working wavelength and the target simulator receiving antenna gain, a target simulator receiving antenna aperture area calculation model is constructed; According to the radar transmitting power, radar transmitting antenna gain and the distance between the target simulator and the radar, a radar radiation power density calculation model for the target simulator installation point is constructed; According to the target simulator receiving antenna aperture area calculation model and the target simulator installation point radar radiation power density calculation model, the target simulator antenna received radar radiation power calculation model is constructed; According to the radar radiation power calculation model received by the target simulator antenna, the transmit-receive radiation ratio of the target simulator and the simulated target transmitting antenna gain generated by the target simulator, a simulation model of the equivalent radiation power of the simulated target generated by the target simulator is constructed; According to the calculation model of the simulated target equivalent radiation power generated by the target simulator and the weather radar equation, a quantitative model of the simulated target equivalent cross-sectional area is constructed.

5. The method according to claim 4, characterized in that The method of constructing a simulated target equivalent cross-sectional area quantitative model based on the simulated target equivalent radiation power calculation model generated by the target simulator and the weather radar equation includes: According to the calculation model of the simulated target equivalent radiation power generated by the target simulator and the first weather radar equation, the radar receiving power calculation model is obtained; According to the radar receiving power calculation model and the second atmospheric radar equation, a quantitative model of the simulated target equivalent cross-sectional area is obtained.

6. The method according to claim 1, characterized in that The method further comprises: Taking the logarithm of the expected value of the radar reflectivity factor, we can obtain the expected value of the radar echo intensity. The radar is calibrated based on the expected value of the radar echo strength.

7. A device for quantitatively generating simulated target reflectivity factor for calibration of weather radar far field, characterized in that: include: Data acquisition module, used to obtain radar antenna and transmission parameters, the distance between the target simulator installation point and the radar, the simulated target distance, and the transmit-receive ratio of the target simulator; A calculation module is used to calculate the equivalent cross-sectional area of ​​the simulated target based on the pre-constructed quantitative model of the equivalent cross-sectional area of ​​the simulated target, based on the radar antenna and transmission parameters, the distance between the target simulator installation point and the radar, the simulated target distance, and the transmit-receive ratio of the target simulator; wherein, The simulated target equivalent cross-sectional area is calculated by the following formula: in, To simulate the target equivalent cross-sectional area, is the target simulator receiving antenna gain, is the target simulator transmit antenna gain, is the transmit-receive ratio of the target simulator, The distance between the target simulator setting point and the radar, Simulate target distances for target simulators, is the radar operating wavelength; the calculation module is also used to calculate the expected value of the radar reflectivity factor according to the equivalent cross-sectional area of ​​the simulated target, the radar antenna and transmission parameters, and the preset weather radar equation; wherein, The expected value of the radar reflectivity factor is calculated by the following formula: Where Z is the expected value of the radar reflectivity factor, is the radar transmit pulse width, θ is the antenna horizontal beam width, is the vertical beam width of the antenna, c is the speed of light, and when the particle is in water state, ≈0.93, when the particle is in the icy state, ≈0.20, R is the simulated target distance; where radar receiving antenna gain = radar transmitting antenna gain = radar antenna gain, that is, G=Gt=Gr.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

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