Method for realizing dual-polarization phased array weather radar target simulation by using digital receiver

Abstract

The invention discloses a method for realizing dual-polarization phased array weather radar target simulation by using a digital receiver, and relates to the technical field of radar signal processing. According to the method for realizing dual-polarization phased array weather radar target simulation by utilizing the digital receiver, a weather target micro-physical parameter model is established, abundant FPGA resources in the phased array radar digital receiver are combined, and a similar direct digital frequency synthesis (DDS) technology is utilized, so that a high-fidelity digital intermediate frequency echo signal is generated; dynamic target simulation is realized in an active array digital receiver of a phased array radar. Controllable and repeatable simulation of various weather phenomena (rain, snow, hail and the like) can be realized in a laboratory environment. Under the condition of not depending on external hardware, echo characteristics of weather targets (such as rainfall particles), including parameters such as distance, speed, amplitude and dual-polarization parameter quantity, are simulated, and debugging, performance verification and algorithm testing of a radar system are effectively supported.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and in particular to a method for simulating targets in a dual-polarization phased array weather radar using a digital receiver. Background Technology

[0002] Phased array weather radar, as a new generation of meteorological observation equipment, achieves rapid and high-resolution detection of precipitation systems through electronic scanning and DBF technology. However, during the development and testing process, traditional debugging methods heavily rely on field tests and natural weather conditions, resulting in numerous limitations. These methods typically require data acquisition and performance verification under real meteorological conditions, leading to low debugging efficiency, high costs, and difficulty in ensuring the consistency and repeatability of test results.

[0003] Traditional debugging requires specific weather phenomena (such as rainfall, snowfall, and wind fields) to occur, making it highly dependent on natural conditions. For example, parameters such as rainfall intensity, particle distribution, and wind shear cannot be generated as needed, resulting in a limited testing window and extended development cycles. In actual testing, radar performance verification requires waiting for suitable weather, which may delay the process by several months, affecting project progress. Furthermore, natural weather events are random and dynamic, making it impossible to accurately reproduce the same meteorological scenarios (such as the microphysical characteristics of thunderstorms and heavy rain), making test data difficult to use for algorithm iteration and comparative analysis. For example, the echo characteristics of the same radar observing the same type of weather at different times may differ significantly due to changes in temperature and humidity, failing to provide a stable benchmark testing environment.

[0004] Radar field tests require airspace coordination, deployment of telemetry and control equipment, and the consumption of live ammunition or weather balloons, with a single test costing up to millions of yuan. Furthermore, geographical and climatic limitations mean test locations may be remote, increasing the investment of manpower, resources, and time. For example, tests targeting polar weather or typhoon scenarios are virtually impossible.

[0005] Traditional methods struggle to simulate extreme or complex weather conditions (such as strong turbulence, hail, and polarized precipitation) and multi-target scenarios (such as mixed rain and snow precipitation), limiting radar performance evaluation in complex environments. Real-world data typically only covers common weather conditions, failing to comprehensively verify the robustness of radar algorithms. Field tests are affected by real-time weather changes, making it impossible to pause, replay, or adjust parameters, hindering fault diagnosis and optimization. Engineers cannot "replay" specific weather events as needed to analyze radar responses, reducing debugging accuracy. Furthermore, methods fail to accurately reflect the scattering characteristics of distributed weather targets, particularly lacking sufficient simulation accuracy for dual-polarization parameters (such as differential reflectivity and differential propagation phase shift).

[0006] While some simulation methods exist in the existing technology (such as microwave anechoic chamber radiation testing or DRFM-based radio frequency injection), they still require some field support and are complex and costly, failing to completely solve the aforementioned problems. Therefore, there is an urgent need for a weather-independent, programmable, and highly reproducible digital simulation technology to improve radar commissioning efficiency and reliability. Summary of the Invention

[0007] The purpose of this invention is to provide a method for simulating targets in a dual-polarization phased array weather radar using a digital receiver, which solves the problems of strong hardware dependence and high cost in existing simulation technologies, and achieves high-fidelity, programmable simulation of weather target echoes, supporting efficient verification of radar systems.

[0008] The core of this invention lies in utilizing the abundant FPGA resources of the digital intermediate frequency (IF) receiver in the DBF phased array radar receiver array. In the digital receiver, the target echo signal received by each array element is simulated in the form of a digital IF signal. The phase information corresponding to the azimuth and elevation angles of the target relative to the receiving array surface is added to the echo signal, forming a target simulation scheme for the digital IF echo signal of each receiving branch. The specific scheme is implemented according to the following steps, and the detailed process is as follows: Figure 1 As shown: The raindrop spectrum is described using gamma distribution, and a microphysical model of precipitation particles is established to simulate the spectrum of non-spherical precipitation particles. Different raindrop spectral distribution (DSD) parameters can be set for different precipitation types (stratified cloud precipitation, convective rainstorms, hail, etc.).

[0009] The T-Matrix method was used to calculate the scattering matrix of non-spherical particles, considering the particle's axial ratio, dielectric constant, and spatial orientation. A scattering parameter database was established, containing scattering characteristic data under different temperature and humidity conditions.

[0010] Based on the particle scattering model, the radar reflectivity of the H and V polarized echo signals is calculated. Differential reflectivity factor Differential phase constant Correlation coefficient Dual polarization parameters observable by radar.

[0011] By setting environmental parameters such as temperature, pressure, and humidity, an atmospheric refractive index model is established. Considering variations in meteorological conditions at different altitudes, the signal propagation characteristics under real atmospheric conditions are simulated.

[0012] Based on the above parameters, the simulation parameters are configured, including the phase accumulator bit width, frequency control word (simulating Doppler frequency shift and echo distance), phase control word (simulating path difference), and other characteristics of the simulated target echo signal.

[0013] Phased array beamforming simulation. The path difference between array elements caused by the target azimuth angle θ and elevation angle φ is calculated, and a phase compensation term is generated to simulate the beam scanning effect.

[0014] Using the internal logic resources of the phased array radar digital receiver, a digital intermediate frequency analog signal is generated according to the control parameters, and injected into the digital signal processing channel of each array element of the DBF receiving array. After being superimposed with the background data, it is used for subsequent signal processing and verification.

[0015] Compared with the prior art, the present invention has the following outstanding advantages: Achieving fully parameter-controllable simulation, overcoming the limitation of traditional simulation methods that can only simulate reflectivity factors, and realizing the simulation of differential reflectivity factors. Calculate the differential phase constant Calculate the correlation coefficient It also supports comprehensive expansion of coverage scenarios, supporting simulation of 12 types of precipitation scenarios, from drizzle to severe convection and hail.

[0016] By innovatively integrating multidisciplinary technologies, microphysics models, electromagnetic scattering theory and DDS digital signal processing technology are deeply combined to establish a complete simulation link from the particle scale to the radar system level.

[0017] Testing costs are significantly reduced, replacing 90% of field tests, and hardware and algorithm debugging can be completed in the laboratory, reducing dependence on natural weather and shortening debugging time.

[0018] By fully utilizing the existing FPGA hardware resources of the phased array radar, native integration of target simulation functions was achieved. This built-in architecture avoids the interface compatibility issues associated with traditional external simulation devices, forming a complete closed-loop testing system. The system directly embeds the simulation function module into the signal processing link, realizing the sharing and optimized utilization of hardware resources. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0020] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a block diagram of the DDS principle used in this invention; Figure 3 This is a block diagram illustrating the core principle of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] Example 1: This embodiment corresponds to Figure 1 The process shown provides a general simulation method applicable to laboratory debugging of various phased array weather radars.

[0023] like Figure 1 As shown, the method of the present invention includes the following steps: S1. Establish a microphysical model for precipitation particles. The raindrop spectrum (DSD) is described using the gamma distribution to simulate the spectrum of non-spherical precipitation particles. Different shape parameters are set for different meteorological targets (such as stratiform cloud precipitation, convective rainstorms, hail, etc.). Slope parameters and concentration parameters The gamma distribution expression is: in, It refers to the number of raindrops per unit volume and unit diameter interval. Let be the equivalent sphere diameter of the ellipsoidal particle. This is the median diameter.

[0024] S2. Construct a scattering parameter database Considering the non-spherical nature of precipitation particles, the T-Matrix method is used to calculate the particle scattering matrix. The calculation requires setting the particle's axial ratio, dielectric constant (which is temperature and frequency related), and spatial orientation distribution. Scattering matrix elements under different temperature, humidity, and incident angle conditions are stored in a database for real-time retrieval.

[0025] S3. Calculate the parameters of the dual-polarization radar. Based on the aforementioned scattering matrix, the radar-observable dual polarization parameters are calculated, serving as the basis for generating the echo signal. The calculation formulas include: Horizontal / Vertical Reflectivity Factor ; Differential reflectivity ; Differential propagation phase shift rate ,in , The propagation constant; the correlation coefficient This characterizes the correlation between horizontal and vertical polarization signals.

[0026] S4. Establish an environment and communication model By setting atmospheric environmental parameters (temperature, pressure, humidity), and using a refractive index model to simulate the propagation characteristics of electromagnetic waves in the real atmosphere, attenuation and phase delay that vary with distance are introduced.

[0027] S5. Configure DDS simulation parameters (mapping process) This is a crucial step in connecting the physical world with digital signals. The physical parameters calculated above are mapped to the control words of the FPGA's internal DDS (Direct Digital Frequency Synthesis) module: Frequency control word (FCW): Determined by the radar operating frequency, target range (time trigger corresponding to round-trip delay), and target radial velocity (Doppler shift).

[0028] Phase control word (POW): Used to simulate the path difference between array elements and the propagation phase.

[0029] Amplitude Control Word (ACW): Derived from reflectivity factor , The received power is determined by the radar equations.

[0030] S6. Phased array beamforming simulation and element phase compensation For phased array systems, calculate the azimuth angle of the target relative to the radar array. and pitch angle For those located at array coordinates The Each array element requires an additional phase compensation term to receive its signal. This is to simulate the path difference of a plane wave arriving at different array elements.

[0031] S7. Signal Injection and Closed-Loop Testing like Figure 3 As shown, the digital intermediate frequency (IF) signal is generated in real time according to the control word mentioned above, utilizing idle or dedicated FPGA logic resources in the digital receiver. This analog signal is then superimposed with the background noise (or ground clutter) acquired by the ADC, and subsequently fed into the digital downconversion (DDC) and digital beamforming (DBF) modules.

[0032] Example 2: This embodiment uses an X-band dual-polarization phased array weather radar as an example to illustrate how to simulate a specific "convective rainfall" and provides specific parameter settings.

[0033] 1. Radar platform parameter settings: Operating frequency: X-band (10 GHz); Array size: The DBF receiver array contains Array element; Element spacing: half wavelength; Intermediate frequency: 100 MHz; Sampling rate: 400 MHz; System bandwidth: 30 MHz.

[0034] 2. Setting up the target weather scenario: The simulation target is set as typical convective rainfall, with the following parameters: Rainfall rate: 50 mm / h; Target distance: 10 km; Radial velocity: m / s (away from radar); Spectral width: 2 m / s.

[0035] 3. Calculation of microphysical parameters (details of steps S1-S3): The Pruppacher-Beard model was used to describe the particle axis ratio. The raindrop spectral parameters were set as follows: Concentration parameters ; Shape parameters ; Median diameter .

[0036] The theoretical values ​​of the radar observation parameters of the rainfall target are obtained by calculating and integrating using the T-Matrix method: .

[0037] 4. Array signal generation and injection (details of steps S5-S7): In the FPGA, the system generates based on the above parameters. Independent digital signals for each path.

[0038] Amplitude control: The base amplitude of each channel is calculated based on the radar equation, and the normalized amplitude is 1 / 3 of the total amplitude. .

[0039] Phase control: Assuming the target is located in the radar normal direction. The primary phase is determined by the propagation distance; if the target deviates from the normal, then for targets located at... The array elements, with added phase .

[0040] Correction of amplitude and phase errors: The system also superimposes pre-measured channel amplitude and phase error correction values. This ensures that the analog signal not only includes meteorological characteristics but also reflects the channel consistency of the radar hardware itself.

[0041] Ultimately, the generated digital echo is directly merged with the ADC data stream within the radar's digital receiver FPGA, allowing the radar display terminal to observe a rainfall echo with an intensity of 45 dBZ at a distance of 10 km without the need for an external signal source.

[0042] Example 3: This embodiment focuses on illustrating the hardware logic architecture for executing the above method, corresponding to Figure 2 and Figure 3 .

[0043] This system relies on the existing digital receiver hardware of the phased array radar, requiring no additional physical equipment. Its core logic module is integrated into the FPGA chip of the receiving channel, and mainly includes the following parts: Parameter parsing and control module: Receive environmental parameters and radar operating parameters sent by the host computer, and calculate the Doppler control word (corresponding to frequency step), range control word (corresponding to trigger time), and beam pointing control word (corresponding to initial phase) required for each pulse cycle (PRT).

[0044] DDS core generation module (such as Figure 2 ): Phase accumulator: Accumulates based on the frequency control word to simulate Doppler frequency shift.

[0045] Phase register: Receives the phase control word and adds it to the output of the accumulator via an adder. This not only simulates the electromagnetic wave propagation phase but also superimposes a specific array manifold phase on each array element channel, thereby achieving beam scanning simulation.

[0046] Sine / Cosine Lookup Table (LUT): Converts phase values ​​to sine wave amplitude values.

[0047] Signal conditioning and injection modules (such as) Figure 3 ): Amplitude Controller: Multiplies the waveform obtained from the lookup table by the amplitude control word. For dual-polarization simulation, different amplitude control words are applied to the H and V channels respectively to reflect... characteristic.

[0048] Adder / Injection Point: Located after the ADC acquisition interface and before the DDC (Digital Down-Converter) module. The analog-generated digital intermediate frequency signal is either selected or superimposed on the real ambient noise (thermal noise, background clutter) quantized by the ADC at this point.

[0049] Advantages: This architecture allows switching between "pure analog mode" (either of two) and "hybrid overlay mode" (overlay), the former for functional verification and the latter for anti-interference and detection probability performance testing.

[0050] With the above architecture, the system can reproduce dual-polarization meteorological echoes with high-fidelity microphysical characteristics at the receiving end in a purely digital manner without emitting electromagnetic waves, which can be used for self-testing and algorithm verification of radar systems.

[0051] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for simulating targets in a dual-polarization phased array weather radar using a digital receiver, characterized in that, Includes the following steps: S1: Simulated non-spherical precipitation particle spectrum: The gamma distribution is used to describe the raindrop spectrum, a microphysical model of precipitation particles is established, and raindrop spectrum distribution parameters are set for different precipitation types; S2: Calculate the scattering matrix: The scattering matrix of non-spherical particles is calculated using the T-Matrix method, taking into account the particle's axial ratio, dielectric constant, and spatial orientation, and a scattering parameter database is established. S3: Calculate dual polarization parameters: Based on the scattering matrix, calculate the radar-observable dual polarization parameters, including the horizontal reflectivity factor. Vertical reflectivity factor Differential reflectivity Differential propagation phase shift rate and correlation coefficient ; S4: Set environmental parameters: Configure temperature, pressure, and humidity environmental parameters, establish an atmospheric refractive index model, and simulate the signal propagation characteristics under real atmospheric conditions; S5: Configure simulation parameters: Based on the dual polarization parameters and environmental parameters, set the control parameters of the digital frequency synthesis technology, including the phase accumulator bit width, frequency control word and phase control word, to simulate the characteristics of the target echo signal; S6: Phased array beamforming simulation: Calculate the path difference between array elements caused by the target azimuth angle θ and elevation angle ϕ, generate phase compensation terms, and simulate beam scanning effect; S7: Signal Injection and Processing: Utilizing the internal logic resources of the phased array radar digital receiver, a digital intermediate frequency analog signal is generated according to the control parameters and injected into the digital signal processing channels of each array element corresponding to the DBF receiving array. After being superimposed with the background data, subsequent signal processing and verification are performed.

2. The method according to claim 1, characterized in that, The gamma distribution mentioned in step S1 is expressed as: in, It is the interval per unit volume and per unit diameter ( arrive The number of raindrops in the container. For concentration parameters, For the shape parameters of the spectrum, It is the median diameter of the drop spectrum. It is the equivalent sphere diameter of the ellipsoidal particle.

3. The method according to claim 1, characterized in that, The T-Matrix method described in step S2 includes: calculating the scattering matrix elements based on the particle axis ratio and dielectric constant, and storing the scattering characteristic data under different temperature and humidity conditions in a database.

4. The method according to claim 1, characterized in that, The calculation of the dual polarization parameters in step S3 includes: Calculate the horizontal reflectivity factor based on the backscattering cross-sections of horizontal and vertical polarization. and vertical reflectivity factor ; Through formula Calculate the differential reflectivity factor; Through formula Calculate the differential propagation phase shift rate, where , These are the propagation constants for horizontal and vertical polarization; The correlation coefficient was calculated based on the cross-correlation characteristics of the horizontal and vertical polarization scattering amplitudes. .

5. The method according to claim 1, characterized in that, The specific function of the control parameters mentioned in step S5 is as follows: The frequency control word is used to simulate the Doppler frequency shift of the target and range information based on echo delay; The phase control word is used to simulate the combined phase change caused by the path difference between array elements, the propagation path phase, and the phase difference of dual polarization scattering. The amplitude control word is used to simulate the echo power determined by the radar equations and The resulting difference in amplitude.

6. The method according to claim 1, characterized in that, The signal processing described in step S7 includes: processing the intermediate frequency signal through a digital down-conversion module and using DBF technology to complete beamforming, thereby achieving target detection against backgrounds of atmospheric noise, thermal noise, meteorological noise, and ground clutter.

7. A system for implementing the method of any one of claims 1-6, characterized in that, include: The microphysics modeling module is used to execute steps S1 and S2 to establish a precipitation particle model and scattering parameter database. The parameter calculation module is used to execute steps S3 and S4 to calculate the radar dual polarization parameters and environmental propagation characteristics. The digital signal generation module, integrated into the digital receiver FPGA of the phased array radar, is used to execute steps S5 and S6. It includes a phase accumulator, a sine lookup table and control logic, and uses DDS technology to generate a digital intermediate frequency signal with specific amplitude, phase and frequency characteristics. The signal injection and processing module is used to execute step S7, injecting the analog signal into the digital signal processing link to complete beamforming and radar function verification.

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