An artificial intelligence-based all-digital radio height-finding radar optimization method

Abstract

This invention discloses an artificial intelligence-based fully digital radio altimeter radar optimization method, relating to the field of altimeter radar data processing technology. The method includes the following steps: performing a first analysis of the natural environment interference effects on the altimeter radar for cross-sea bridges, and a first optimization of the interference superposition; performing a second analysis of the natural environment interference effects on the altimeter radar for cross-sea bridges, and a second optimization of the interference superposition; performing feedback analysis of the optimization and making a judgment and early warning. This invention analyzes the signal-to-noise ratio decrease due to the combined effect coefficient; if it exceeds the standard, the pulse repetition frequency is adjusted, and then the bridge vibration and coupling frequencies are analyzed to determine the overlap type with vehicle frequencies. Based on this, the filter cutoff frequency and pulse repetition frequency are adjusted, achieving an improved robustness against nonlinear, strongly coupled multi-physics field interference.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar data processing, and particularly relates to a full-digital radio altimeter optimization method based on artificial intelligence. BACKGROUND

[0002] Radio altimeter is a sensor that calculates vertical distance based on signal time delay or frequency difference by transmitting electromagnetic waves and receiving target reflection echoes. Its core advantage lies in non-contact measurement and high precision, and it is widely used in the fields of aviation, unmanned aerial vehicles and bridge health monitoring. In the field of bridge health monitoring, radio altimeter is regarded as a low-cost and high-reliability vertical displacement measurement tool. By measuring the vertical distance change of the sensor to the bridge surface, the deformation of the bridge under load is inverted. By analyzing the time / frequency domain characteristics of high-frequency vibration signals, the health status of the bridge structure is evaluated. Combined with the correlation between vehicle vibration signals and bridge vibration signals, the type of vehicles passing through the bridge and the axle load distribution are indirectly identified.

[0003] However, the technical characteristics of traditional radio altimeter make it face significant limitations in the monitoring scene of cross-sea bridge in complex marine environment, and it is difficult to meet the long-term and high-reliability monitoring requirements. Due to the complexity of marine environment and high intensity of vehicle load, the performance of radio altimeter for cross-sea bridge is far beyond the requirements of conventional scenes. The limitations of traditional technology are mainly reflected in the following aspects: insufficient environmental corrosion and hardware reliability, serious multipath interference and signal aliasing, and dynamic signal aliasing and coupling interference.

[0004] For example, the invention patent with publication number CN119940267A discloses a standard module-based inertial control system integrated design method. Based on demand analysis, function module division, and construction of standard modules, the internal and external electrical interfaces of standard modules are standardized, the mechanical interfaces of standard modules are standardized, the structure layout of integrated inertial control system is determined, and the force / thermal environmental adaptability of integrated inertial control system is analyzed to complete integrated design iteration optimization. This method redefines the functions of traditional missile-borne integrated control machines, inertial navigation systems, satellite receivers, radio altimeters, barometric altimeters and other discrete electronic devices, constructs standard modules, and unifies power distribution and management, centralized information processing and structure integration design.

[0005] For example, the invention patent announcement CN109992897B discloses a radio altimeter radar simulation method for ground proximity warning devices. This method includes a main control unit, a calculation unit, and a signal unit. The main control unit is an industrial control computer used to receive user commands, process them, and forward them to the calculation unit. The calculation unit is also an industrial control computer used to process the received commands and send them to the signal unit. The calculation unit contains calculation software. The signal unit is a board that can emit ARINC429 signals, installed in the calculation unit, used to emit altimeter radar simulation signals. The user opens the simulation software on the main control unit, inputs simulated flight altitude information, and the simulation software sends this information to the calculation software in the calculation unit. The calculation software processes the simulated altitude information to form simulation information and sends it to the signal unit. The signal unit translates the received simulation information into an ARINC429 signal and sends it to the ground proximity warning device as the simulated altitude information.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0007] In existing technologies, traditional radio altimeter radars, when used for vehicle load monitoring on cross-sea bridges, suffer from limitations in their simple signal processing algorithms and weak environmental adaptability, making it difficult to meet complex requirements. When dealing with environmental corrosion, multipath interference, dynamic signal aliasing, long-term drift, and severe weather, the error rate spikes significantly. Traditional hardware designs are not optimized for complex marine environments, and signal processing algorithms rely on simple thresholds and linear filtering, resulting in insufficient measurement accuracy and reliability. Furthermore, fully digital radio altimeter radars lack robustness when dealing with nonlinear and strongly coupled multi-physics interference. Summary of the Invention

[0008] This application provides an AI-based optimization method for fully digital radio altimetry radar, which solves the problem of insufficient robustness of existing fully digital radio altimetry radars when dealing with nonlinear and strongly coupled multi-physics field interference, and achieves the effect of improving robustness when dealing with nonlinear and strongly coupled multi-physics field interference.

[0009] This application provides an artificial intelligence-based fully digital radio altimeter radar optimization method, including the following steps: performing a first analysis of the natural environment interference effect of the altimeter radar for cross-sea bridges, and performing a first optimization of the interference superposition of the altimeter radar for cross-sea bridges; performing a second analysis of the natural environment interference effect of the altimeter radar for cross-sea bridges, and performing a second optimization of the interference superposition of the altimeter radar for cross-sea bridges; performing feedback analysis of the optimization and making a judgment and early warning.

[0010] Furthermore, a first analysis of the natural environment interference effect of the altimeter radar for the cross-sea bridge is conducted, specifically including: collecting the ambient humidity of the altimeter radar through temperature and humidity sensors; directly obtaining the ambient rainfall intensity of the altimeter radar through a meteorological database; directly extracting the humidity influence coefficient and rainfall influence coefficient from the comprehensive database of radio altimeters radar; if the ambient humidity of the altimeter radar is greater than the ambient humidity threshold and the ambient rainfall intensity of the altimeter radar is greater than the ambient rainfall intensity threshold, then the signal-to-noise ratio reduction analysis value of the altimeter radar for the cross-sea bridge is obtained through a comprehensive analysis of the ambient humidity, ambient rainfall intensity, humidity influence coefficient, and rainfall influence coefficient.

[0011] Furthermore, the first optimization of interference superposition between the altimeter radar and the cross-sea bridge is performed, specifically including: directly extracting the reference signal-to-noise ratio (SNR) of the altimeter radar from the historical database of radio altimeter radar; comparing the reference SNR with the SNR decrease analysis value of the cross-sea bridge altimeter radar to obtain the lower limit of SNR fluctuation; if the lower limit of SNR fluctuation is greater than the SNR failure value, optimization is not triggered; if the lower limit of SNR fluctuation is equal to or less than the SNR failure value, optimization is triggered; directly extracting the minimum value of the altimeter radar pulse repetition frequency, the reference value of the altimeter radar pulse repetition frequency, and the maximum value of ... The maximum signal-to-noise ratio (SNR) loss threshold is determined by comparing the baseline value of the altimeter radar pulse repetition frequency with the minimum value of the altimeter radar pulse repetition frequency, and recording this as the adjustment range value of the altimeter radar pulse repetition frequency. The results of the proportional analysis of the unit-level and cross-sea bridge altimeter radar SNR fluctuation lower limit value with the maximum SNR loss threshold are compared to obtain the altimeter radar SNR fluctuation loss coefficient. The minimum value of the altimeter radar pulse repetition frequency, the adjustment range value of the altimeter radar pulse repetition frequency, and the altimeter radar SNR fluctuation loss coefficient are coupled and analyzed to obtain the optimized value of the altimeter radar pulse repetition frequency. Trigger optimization is used to determine the pulse repetition frequency output by the altimeter radar based on the optimized value of the altimeter radar pulse repetition frequency.

[0012] Furthermore, a second analysis of the natural environment interference effect of the altimeter radar for the cross-sea bridge is conducted. Specifically, this includes: if the ambient wind speed is less than or equal to the altimeter radar's ambient wind speed threshold, the second analysis is not performed; if the ambient wind speed exceeds the altimeter radar's ambient wind speed threshold, then the second analysis is performed. The second analysis of the natural environment interference effect of the altimeter radar for the cross-sea bridge includes: directly extracting the Strouhal number, characteristic width, and nonlinear stiffness coefficient of the cross-sea bridge from the integrated database of radio altimeters radar; and obtaining data from the anemometer at the top of the radar antenna mast. The following methods were used to obtain the sea wind speed of the cross-sea bridge: Inertial measurement sensors installed at the bottom of the bridge box girder measured the vibration displacement in real time, and the amplitude was extracted through fast Fourier transform analysis to obtain the maximum vibration amplitude of the bridge; the Strauhal number of the cross-sea bridge was coupled with the sea wind speed and then compared with the characteristic width of the bridge to obtain the linear vibration frequency of the bridge; the coupling results of the unit coefficient and the nonlinear stiffness coefficient of the bridge with the maximum vibration amplitude of the bridge were compared and then subjected to square root analysis to obtain the nonlinear vibration frequency adjustment coefficient of the bridge; the linear vibration frequency of the bridge and the nonlinear vibration frequency adjustment coefficient were coupled and analyzed to obtain the nonlinear vibration frequency of the bridge.

[0013] Furthermore, the second analysis of the natural environment interference effect of the cross-sea bridge height measurement radar also includes: directly extracting the bridge mass, bridge stiffness, and fitted damping ratio from the integrated database of radio height measurement radar; obtaining the total average mass of vehicles on the bridge through real-time measurement by vehicle weighing systems installed at the bridge entrances and exits; after identifying specific vehicle models, directly obtaining the corresponding vehicle stiffness from the integrated database of radio height measurement radar and then summing and averaging it to obtain the total average stiffness of the vehicles; and coupling the fitted damping ratio with the square root analysis results of the bridge mass and the total average mass of the vehicles to obtain the total coupled damping coefficient of the bridge and vehicles. The bridge-vehicle coupling frequency is calculated by comparing the bridge stiffness with the total average stiffness of the vehicles and then performing a square root analysis after coupling with the total bridge-vehicle coupling damping coefficient. This square root is then used to determine the bridge-vehicle coupling attenuation coefficient. The bridge stiffness and the total average stiffness of the vehicles are then compared with the bridge-vehicle coupling attenuation coefficient, and the results are further analyzed by combining them with the bridge mass and the total average mass of the vehicles to obtain the basic value of the bridge-vehicle coupling frequency. This basic value is then square rooted and coupled with a predefined coefficient to obtain the bridge-vehicle coupling frequency. Finally, the frequency coupling degree is analyzed based on the vehicle vibration dominant frequency, the bridge nonlinear vibration frequency, and the bridge-vehicle coupling frequency.

[0014] Furthermore, frequency coupling is analyzed based on the vehicle vibration dominant frequency, bridge nonlinear vibration frequency, and bridge-vehicle coupling frequency. Specifically, this includes: directly extracting the vehicle vibration dominant frequency from the integrated radio altimeter radar database; comparing the overlap interval length of any two frequencies with the maximum coverage length of the three frequencies (vehicle vibration dominant frequency, bridge nonlinear vibration frequency, and bridge-vehicle coupling frequency) to obtain the overlap between the bridge nonlinear vibration frequency and the vehicle vibration dominant frequency, the bridge-vehicle coupling frequency and the vehicle vibration dominant frequency, and the bridge nonlinear vibration frequency and the bridge-vehicle coupling frequency; if the overlap between the bridge nonlinear vibration frequency and the vehicle vibration dominant frequency is greater than the overlap between the bridge-vehicle coupling frequency and the vehicle vibration dominant frequency... If the overlap between the nonlinear vibration frequency of the bridge and the dominant vibration frequency of the vehicle is greater than the overlap between the nonlinear vibration frequency of the bridge and the vehicle coupling frequency of the bridge, it is judged as bridge-vehicle dominant overlap. If the overlap between the vehicle coupling frequency of the bridge and the dominant vibration frequency of the vehicle is greater than the overlap between the nonlinear vibration frequency of the bridge and the dominant vibration frequency of the vehicle, and the overlap between the vehicle coupling frequency of the bridge and the dominant vibration frequency of the vehicle is greater than the overlap between the nonlinear vibration frequency of the bridge and the vehicle coupling frequency of the bridge, it is judged as bridge-vehicle coupling and vehicle-dominant overlap. If the overlap between the nonlinear vibration frequency of the bridge and the dominant vibration frequency of the vehicle, the overlap between the vehicle coupling frequency of the bridge and the dominant vibration frequency of the vehicle, and the overlap between the nonlinear vibration frequency of the bridge and the vehicle coupling frequency of the bridge are all greater than the overlap judgment threshold, it is judged as full-band overlap.

[0015] Furthermore, a second optimization of the interference superposition of the cross-sea bridge height measurement radar is performed, specifically including: if it is determined that the bridge and vehicle are the main overlapping factors, the results of the comparative analysis of the vehicle vibration main frequency and the first frequency of the safety interval and the coupling analysis of the bridge nonlinear vibration pseudo-peak frequency and the first frequency of the safety interval are processed by the maximum value function to obtain the first frequency of the multipath suppression filter cutoff adjustment. The cutoff frequency of the height measurement radar multipath suppression filter is determined based on the first frequency of the multipath suppression filter cutoff adjustment. The first adjustment coefficient of the threshold, the overlap between the bridge vehicle coupling frequency and the vehicle vibration main frequency and the coupling result of the unit are processed by the logarithmic function to obtain the test adjustment detection value. The original radar detection threshold and the test adjustment detection value are compared and analyzed to obtain the radar adjustment detection threshold. The height measurement radar adjustment detection threshold is determined based on the radar adjustment detection threshold.

[0016] Furthermore, the second optimization of interference superposition for cross-sea bridge height measurement radar includes: if it is determined that the bridge-vehicle coupling and vehicle-dominant frequency overlap, the results of the coupling analysis between the vehicle vibration dominant frequency and the first frequency of the safety interval and the results of the comparative analysis between the bridge-vehicle coupling frequency and the second frequency of the safety interval are processed by the minimum value function to obtain the second frequency of multipath suppression filter cutoff adjustment. The cutoff frequency of the height measurement radar multipath suppression filter is determined based on the second frequency of multipath suppression filter cutoff adjustment. The first frequency of height measurement radar pulse repetition frequency adjustment is obtained by using the reference value of height measurement radar pulse repetition frequency, pulse repetition frequency adjustment coefficient, overlap between bridge-vehicle coupling frequency and vehicle vibration dominant frequency and unit coupling analysis. The height measurement radar pulse repetition frequency is adjusted based on the first frequency of height measurement radar pulse repetition frequency adjustment.

[0017] Furthermore, the second optimization of interference superposition in the height measurement radar for cross-sea bridges includes: if it is determined to be full-band overlap, then the nonlinear vibration frequency of the bridge, the vehicle coupling frequency of the bridge, and the main vibration frequency of the vehicle are processed by the median function to obtain the third frequency for multipath suppression filter cutoff adjustment; the band-stop filter frequency of the height measurement radar multipath suppression filter is determined based on the second frequency for multipath suppression filter cutoff adjustment; the second adjustment coefficient of the threshold, the frequency overlap of the three and the coupling result of unit 1 are processed by the logarithmic function and recorded as the frequency coupling adjustment coefficient of the three to obtain the first value for verification and calibration detection; the original radar detection threshold is compared and analyzed with the first value for verification and calibration detection to obtain the first threshold for radar calibration detection; the height measurement radar calibration detection threshold is determined based on the first threshold for radar calibration detection; the first frequency for height measurement radar pulse repetition frequency adjustment is obtained by analyzing the reference value of the height measurement radar pulse repetition frequency, the first adjustment coefficient of the pulse repetition frequency, the frequency overlap of the three and the coupling of unit 1; the height measurement radar pulse repetition frequency is adjusted based on the first frequency for height measurement radar pulse repetition frequency adjustment.

[0018] Furthermore, optimized feedback analysis and judgment and early warning are carried out, specifically including: after optimization and adjustment, if the final signal-to-noise ratio of the cross-sea bridge height measurement radar is less than the preset effective signal-to-noise ratio threshold, or the final false detection rate of vehicle detection is still greater than the preset false detection rate target value, or the overlap of bridge vehicle coupling frequency is still greater than the preset safe overlap threshold, then the audible and visual alarm device of the radar system is activated, and an email is simultaneously pushed to the maintenance personnel terminal. The execution results of all current adjustment steps, environmental parameters, and bridge vehicle coupling parameters are stored in non-volatile memory.

[0019] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0020] 1. This optimization method addresses the anti-interference requirements of cross-sea bridge height measurement radar by resolving the interference issues arising from the natural environment and vehicle-bridge coupling in stages. First, ambient humidity and rainfall intensity are obtained using temperature and humidity sensors and a meteorological database. The signal-to-noise ratio (SNR) is assessed based on humidity and rainfall impact coefficients. If these parameters exceed the limit, the first optimization step of interference superposition is triggered, adjusting the pulse repetition frequency. Then, when wind speed exceeds the limit, the linear and nonlinear vibration frequencies of the bridge are calculated using parameters such as the bridge's Strouhal number and characteristic width. Next, the bridge-vehicle coupling frequency is calculated using parameters such as vehicle mass and stiffness. The frequency coupling degree between the bridge and vehicle-dominated systems, bridge-vehicle coupling and vehicle-dominated systems, or the entire frequency band is analyzed. Based on different overlap types, the cutoff frequency of the multipath suppression filter is adjusted in multiple stages using different functions, and the pulse repetition frequency is adjusted using the SNR fluctuation loss coefficient. If the optimized SNR is still insufficient, the false detection rate is too high, or the overlap exceeds the limit, an audible and visual alarm is activated, and data is recorded to ensure stable radar operation in complex environments.

[0021] 2. Focusing on signal attenuation issues in the complex environment of cross-sea bridges. By collecting environmental parameters such as humidity and rainfall in real time, and combining them with pre-calibrated environmental influencing factors, the degree of signal-to-noise ratio (SNR) degradation of the radar received signal is comprehensively assessed. When the SNR is determined to be below the stable detection threshold, the pulse repetition frequency is dynamically adjusted: if the SNR loss is small, the pulse repetition frequency is maintained at the baseline value to ensure signal stability; if the loss is large, the pulse repetition frequency is gradually reduced to the minimum allowable value to avoid false detections caused by signal overlap. This process effectively addresses signal attenuation issues under severe weather conditions such as high salt spray and heavy rainfall, improves the radar's stable detection capability in complex environments, and lays the foundation for subsequent interference suppression.

[0022] 3. When the ambient wind speed exceeds the set threshold, the coupling effect between the low-frequency nonlinear vibration of the bridge caused by strong wind and the vehicle vibration is analyzed. The bridge-vehicle coupling frequency is calculated, and the overlap type of the three frequencies is determined by combining the dominant frequency of the vehicle vibration. Based on the overlap type, the cutoff frequency of the multipath suppression filter, the detection threshold of the altimeter radar, and the pulse repetition frequency are adjusted accordingly. This effectively separates the nonlinear pseudo-peaks of the bridge, the coupling pseudo-peaks, and the vehicle signal, solving the signal aliasing problem caused by strong coupling of multiple physical fields and significantly improving the detection accuracy of the radio altimeter radar.

[0023] 4. This step ensures optimization effectiveness through closed-loop feedback. After optimization, if the radar's final signal-to-noise ratio is still below the effective threshold, the vehicle false detection rate fails to meet the standard, or the bridge-vehicle coupling overlap is still too high, the system triggers an audible and visual alarm and pushes a warning message to maintenance personnel, simultaneously storing the adjustment parameters, environmental parameters, and coupling parameters. This step enables real-time tracking of optimization effects and problem tracing, facilitating timely troubleshooting of hardware aging and ensuring the long-term reliability of the system. Attached Figure Description

[0024] Figure 1A flowchart of an AI-based fully digital radio altimeter radar optimization method provided in this application embodiment. Detailed Implementation

[0025] This application provides an AI-based optimization method for fully digital radio altimeter radar, which addresses the problem of insufficient robustness of existing fully digital radio altimeter radars in dealing with nonlinear and strongly coupled multi-physics interference. The method addresses issues such as reduced signal-to-noise ratio in the analysis of the junction influence coefficient; if the signal-to-noise ratio exceeds the limit, the pulse repetition frequency is adjusted; then, the bridge vibration and coupling frequencies are analyzed to determine the overlap type with vehicle frequencies; and the filter cutoff frequency and pulse repetition frequency are adjusted accordingly. This improves the robustness against nonlinear and strongly coupled multi-physics interference.

[0026] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0027] like Figure 1 The diagram shown is a flowchart of an artificial intelligence-based fully digital radio altimeter radar optimization method provided in this application embodiment. The method includes the following steps: performing a first analysis of the natural environment interference effect of the altimeter radar for cross-sea bridges, and performing a first optimization of the interference superposition of the altimeter radar for cross-sea bridges; performing a second analysis of the natural environment interference effect of the altimeter radar for cross-sea bridges, and performing a second optimization of the interference superposition of the altimeter radar for cross-sea bridges; performing feedback analysis of the optimization and making a judgment and early warning.

[0028] In this embodiment, traditional radio altimeter radar transmits electromagnetic waves and receives reflected echoes from targets, calculating vertical distance based on time delay. However, in the scenario of vehicle load monitoring on a cross-sea bridge, its simple signal processing algorithm and poor environmental adaptability are insufficient to meet complex requirements. Specific problems include: environmental corrosion: salt spray and humid environments easily lead to oxidation of antenna feeders and aging of the RF front end, resulting in decreased signal transmission / reception efficiency and a reduced measurement signal-to-noise ratio; multipath interference: reflected signals from the bridge's steel box girder, stay cables, and vehicle's metal body overlap with direct signals, forming multipath pseudo-peaks, which are prone to misjudgment by traditional threshold detection, leading to abrupt changes in measurement results; dynamic signal aliasing: vibrations of the bridge caused by sea winds and waves overlap with the vibration spectrum of vehicle travel, which traditional filtering algorithms cannot separate, and deformation is masked by noise; long-term drift: electronic components drift over time, and the sensor mounting reference surface deforms due to vibration, causing measurement errors to gradually accumulate. Insufficient robustness in severe weather: heavy rain and strong winds exacerbate signal attenuation and air disturbances, causing drastic fluctuations in echo power, reducing the probability of effective ranging, and making it difficult to achieve continuous and stable monitoring.

[0029] Traditional hardware designs are not optimized for complex marine environments, and signal processing algorithms rely on simple thresholds and linear filtering, which cannot cope with nonlinear and strongly coupled multi-physics interference, resulting in insufficient measurement accuracy and reliability.

[0030] The hardware functions of a fully digital radio altimeter radar must be determined during the design phase; otherwise, the protection level or core performance may be compromised. Example requirements are as follows: the antenna feed line should be made of gold-plated copper alloy, and the outer shell should be made of polycarbonate + fiberglass composite material with a fluorocarbon coating. The sensor mounting bracket should be made of stainless steel, with anti-corrosion gaskets used at the fixing points to the bridge body. The corrosion resistance of the materials directly determines the hardware lifespan. For the core sensor type and accuracy of the altimeter radar, a high-precision fiber optic gyroscope should be selected for the inertial measurement unit, and an industrial-grade digital sensor should be selected for the temperature and humidity sensor, supporting I / O... 2 The C / RS485 interface and sensor accuracy determine the benchmark for subsequent signal processing. The RF front-end of the altimeter radar uses a temperature-controlled crystal oscillator, and the antenna is a directional parabolic antenna; the RF module integrates a low-noise amplifier to ensure sensitivity in weak signal reception, and the performance of the RF front-end directly affects the quality of the echo signal.

[0031] Furthermore, a first analysis of the natural environmental interference effects on the altimeter radar for the cross-sea bridge was conducted. This included: collecting ambient humidity data from a temperature and humidity sensor (SHT 31) with an accuracy of ±0.5% RH; directly obtaining the ambient rainfall intensity from a meteorological database; and rainfall (mm / h) from a meteorological station or the radar's own precipitation detection module (e.g., via microwave scattering measurement) with an accuracy of ±1 mm / h. If the ambient humidity and rainfall intensity of the altimeter radar both exceed the radar's ambient humidity threshold, the signal-to-noise ratio (SNR) reduction was analyzed using the cross-sea bridge altimeter radar. SNRbase represents the baseline SNR: the average SNR of the radar in a rain-free and interference-free environment, calibrated to 20 dB using historical data. α represents the humidity influence coefficient; for every 1% increase in humidity (RH), the signal attenuation increases by 0.5 dB, calibrated using historical experimental data, such as when humidity increases from 85% to 95%. The signal attenuation increases by 5dB; β represents the rainfall influence coefficient, where the signal attenuation increases by 3dB for every 1mm / h increase in rainfall, calibrated using historical data from experimental tests; the signal-to-noise ratio failure indicates the SNR threshold: when the SNR is below 10dB, the radar cannot stably detect echoes and optimization needs to be triggered; the SNR reduction constraint formula is as follows: ΔSNR=α*(RH-YZ1)+β*(Prain / YZ2), where ΔSNR represents the SNR reduction analysis value of the cross-sea bridge altimeter radar, YZ1 represents the ambient humidity threshold of the altimeter radar, and YZ2 represents the ambient rainfall intensity threshold of the altimeter radar; RH-YZ1: the portion of humidity exceeding the ambient humidity threshold of the altimeter radar; Prain / YZ2: the multiple of rainfall relative to the ambient rainfall intensity threshold of the altimeter radar.

[0032] In this embodiment, the humidity threshold for the altimeter radar environment can be 85%, which is measured based on historical humidity data at a specific location. Since 85%RH is the critical value for salt spray corrosion at the corresponding location, corrosion accelerates after exceeding this value. The rainfall intensity threshold for the altimeter radar environment can be 50%, which is measured based on historical rainfall data at a specific location. Since 50mm / h is the threshold for heavy rain, interference with the radar increases significantly after exceeding this value.

[0033] Furthermore, the first optimization of the interference superposition of the cross-sea bridge altimetry radar is carried out, specifically including: directly extracting the reference signal-to-noise ratio (SNR) of the altimetry radar from the historical database of the radio altimetry radar. If the reference SNR of the altimetry radar is compared and analyzed with the analysis value of the SNR decrease of the cross-sea bridge altimetry radar, the lower limit value of the SNR fluctuation of the cross-sea bridge altimetry radar can be obtained. If the lower limit value of the SNR fluctuation of the cross-sea bridge altimetry radar is greater than the SNR failure, the optimization is not triggered. If the lower limit value of the SNR fluctuation of the cross-sea bridge altimetry radar is equal to or less than the SNR failure, the optimization is triggered, where SNRthreshold represents the SNR failure. The minimum pulse repetition frequency (PRF), the reference PRF of the altimetry radar, and the maximum SNR loss threshold are directly extracted from the comprehensive database of the radio altimetry radar. The constraint formula for optimizing the PRF of the altimetry radar is as follows: PRFnew = PRFmin + (PRFbase - PRFmin) * (1 - SNRloss / SNRmax_loss), where PRFnew represents the optimized value of the PRF of the altimetry radar, PRFmin represents the minimum PRF of the altimetry radar, which is the lowest PRF at which the radar can stably detect echoes and avoid false detection caused by signal overlap, PRFbase represents the reference PRF of the altimetry radar, which is the PRF of the radar in an interference-free and high-SNR environment, SNRloss represents the lower limit value of the SNR fluctuation of the cross-sea bridge altimetry radar, and SNRmax_loss represents the maximum SNR loss threshold, which is the critical value of the SNR loss that triggers the PRF to drop to the minimum value. After exceeding this value, the PRF no longer decreases.

[0034] In this embodiment, SNRthreshold is directly calibrated according to the experimental values of the corresponding altimetry radar. For example, when the SNR is lower than 10 dB, the radar cannot stably detect echoes and the optimization needs to be triggered.

[0035] When SNRbase - ΔSNR = SNRloss < SNRthreshold, that is, 20 - 3.1 = 16.9 dB > 10 dB, the optimization is not triggered.

[0036] If the rainfall increases to 80 mm / h and RH = 95%, then ΔSNR = 0.5 * 10 + 3 * (30 / 50) = 5 + 1.8 = 6.8 dB, and SNR = 20 - 6.8 = 13.2 dB, still higher than the threshold.

[0037] When the rainfall = 100 mm / h and RH = 100%, ΔSNR = 0.5 * 15 + 3 * (50 / 50) = 7.5 + 3 = 10.5 dB, and SNR = 20 - 10.5 = 9.5 dB < 10 dB, the optimization is triggered.

[0038] When SNRloss = 0, that is, there is no SNR loss, PRFnew = PRFbase, and the reference frequency is maintained.

[0039] When SNRloss = SNRmax_loss, the maximum allowable loss, PRFnew = PRFmin, reducing to the lowest frequency.

[0040] When SNRloss > SNRmax_loss, PRFnew remains at PRFmin to avoid signal overlap caused by excessively low frequencies.

[0041] Optimized logic for smooth transition: PRF decreases linearly with SNR loss, avoiding instability in altimeter radar signal processing caused by sudden environmental changes; Minimum frequency protection: Prevents pulse overlap caused by excessively low PRF, leading to false detection; Strong adaptability: By optimizing PRFmin and SNRmax_loss, it can be adapted to different radar models and environmental requirements.

[0042] Furthermore, a second analysis of the natural environment interference effect of the altimeter radar on the cross-sea bridge is conducted. Specifically, if the ambient wind speed is less than or equal to the altimeter radar's ambient wind speed threshold, the second analysis of the natural environment interference effect is not performed. If the ambient wind speed exceeds the altimeter radar's ambient wind speed threshold, under strong winds, the bridge enters the nonlinear vibration zone due to large amplitude deformation, and its vibration frequency is no longer fixed but decreases with increasing amplitude. The altimeter radar's ambient wind speed threshold can be set to 15 m / s, which can be determined by expert prior knowledge based on the specific bridge. The quantitative formula for the nonlinear vibration of the bridge under strong winds is as follows: Wherein, fbridge_nonlinear represents the nonlinear vibration frequency of the bridge, fbridge_linear represents the linear vibration frequency of the bridge, ∈ represents the nonlinear stiffness coefficient of the bridge, which is dimensionless and characterizes the degree to which stiffness decreases with displacement. The nonlinear stiffness coefficient of the bridge is obtained by applying sinusoidal excitation to the bridge model simulation, measuring the displacement-force curve, and fitting the nonlinear stiffness coefficient. For example, the experimental calibration value is taken as 0.1. A represents the vibration amplitude of the bridge, which is measured in real time by an IMU sensor installed at the bottom of the bridge box girder, and the amplitude is extracted by fast Fourier transform analysis. fbridge_linear = S*v / d. S represents the Strouhal number, which is calibrated to 0.2 for the cross-sea bridge through experiments. v represents the sea wind speed, which is measured in real time by an anemometer at the top of the radar antenna mast. d represents the characteristic width of the bridge, which is obtained from the corresponding bridge design drawings.

[0043] In this embodiment, under strong winds (v>15m / s), the bridge enters the nonlinear vibration region due to large amplitude deformation. Its vibration frequency is no longer fixed but decreases with increasing amplitude (a characteristic of a soft spring). This phenomenon is described using a nonlinear stiffness model. When the bridge amplitude A increases, the nonlinear term ∈*A increases, leading to a decrease in fbridge_nonlinear and a broadening of the frequency. For example, when A = 0.1m, fbridge_nonlinear = 0.9*fbridge_linear.

[0044] It is important to note that the phenomenon of the nonlinear vibration frequency of bridges decreasing with increasing amplitude A under strong winds is consistent with the "soft spring" characteristic of nonlinear stiffness. When a bridge vibrates in strong winds, if the amplitude A exceeds the linear vibration range, the structure will enter the nonlinear large deformation region. At this time, the geometry of the bridge will cause changes in the stiffness of the material or structure: under large displacement of the bridge due to wind, the curvature change leads to a decrease in bending stiffness; steel may enter the plastic deformation region under high stress, reducing the elastic modulus and further reducing stiffness. In this scenario, the bridge vibration will also change the aerodynamic load of vehicles, and the vehicle vibration will react on the bridge, resulting in a superimposed effect. This will further affect the robustness of fully digital radio altimeter radar in dealing with nonlinear, strongly coupled multi-physics interference.

[0045] Furthermore, the second analysis of the natural environment interference effect of the cross-sea bridge height measurement radar also includes: directly extracting the bridge mass, bridge stiffness, and fitted damping ratio from the integrated database of radio height measurement radar; obtaining the total average mass of vehicles on the bridge through real-time measurement by vehicle weighing systems installed at the bridge entrances and exits; after identifying specific vehicle models, directly obtaining the corresponding vehicle stiffness from the integrated database of radio height measurement radar and then summing and averaging it to obtain the total average stiffness of the vehicles; and coupling the fitted damping ratio with the square root analysis results of the bridge mass and the total average mass of the vehicles to obtain the total coupled damping coefficient of the bridge and vehicles. The bridge stiffness and the total average stiffness of the vehicles are compared and analyzed, and then coupled with the total coupling damping coefficient of the bridge and vehicles, followed by square root analysis, which is denoted as the total coupling attenuation coefficient of the bridge and vehicles. The bridge stiffness and the total average stiffness of the vehicles are coupled and analyzed, and then compared with the total coupling attenuation coefficient of the bridge and vehicles. The coupling results are then compared with the bridge mass and the total average mass of the vehicles, and the ratio analysis is performed to obtain the basic value of the bridge-vehicle coupling frequency. The basic value of the bridge-vehicle coupling frequency is square rooted and then coupled with a predefined coefficient to obtain the bridge-vehicle coupling frequency. The frequency coupling degree is analyzed based on the vehicle vibration dominant frequency, the bridge nonlinear vibration frequency, and the bridge-vehicle coupling frequency.

[0046] In this embodiment, Where fcouple represents the bridge-vehicle coupling frequency; M bThe bridge mass is indicated by bridge structural design drawings obtained from a comprehensive database of radio altimetry radar; M v The vehicle weighing system, representing the total average mass of vehicles, is installed at the bridge entrance and exit to measure the total average mass of vehicles in real time; K b The bridge stiffness is indicated by bridge structural design drawings obtained from a comprehensive database of radio altimetry radar; K v The vehicle's total average stiffness is obtained through vehicle dynamics testing, measuring the vehicle body displacement under wheel excitation based on the vehicle's suspension system parameters. After pre-calibration, the corresponding vehicle stiffness is directly obtained after identifying the specific vehicle model, and then summed and averaged. C represents the bridge-vehicle total coupling damping coefficient, representing the damping characteristics of the bridge and vehicle. This is obtained through vibration decay testing. Accelerometers are installed on the bridge, and the test vehicle travels at a constant speed, recording the free decay curve. The damping ratio ζ is then fitted and input into the radio altimeter radar integrated database, and then... Calculated.

[0047] Furthermore, frequency coupling is analyzed based on the vehicle vibration dominant frequency, bridge nonlinear vibration frequency, and bridge-vehicle coupling frequency. Specifically, this includes: directly extracting the vehicle vibration dominant frequency from the integrated radio altimeter radar database; comparing the overlap interval length of any two frequencies with the maximum coverage length of the three frequencies (vehicle vibration dominant frequency, bridge nonlinear vibration frequency, and bridge-vehicle coupling frequency) to obtain the overlap between the bridge nonlinear vibration frequency and the vehicle vibration dominant frequency, the bridge-vehicle coupling frequency and the vehicle vibration dominant frequency, and the bridge nonlinear vibration frequency and the bridge-vehicle coupling frequency; if the overlap between the bridge nonlinear vibration frequency and the vehicle vibration dominant frequency, the bridge-vehicle coupling frequency and the vehicle vibration dominant frequency, and the bridge nonlinear vibration frequency and the bridge-vehicle coupling frequency are all considered, then... If the overlap of vehicle coupling frequencies is greater than the overlap threshold, it is judged as full-band overlap. If the overlap between the bridge nonlinear vibration frequency and the vehicle vibration main frequency is greater than the overlap between the bridge vehicle coupling frequency and the vehicle vibration main frequency, and the overlap between the bridge nonlinear vibration frequency and the vehicle vibration main frequency is greater than the overlap between the bridge nonlinear vibration frequency and the bridge vehicle coupling frequency, it is judged as bridge-vehicle dominant overlap. If the overlap between the bridge vehicle coupling frequency and the vehicle vibration main frequency is greater than the overlap between the bridge nonlinear vibration frequency and the vehicle vibration main frequency, and the overlap between the bridge vehicle coupling frequency and the vehicle vibration main frequency is greater than the overlap between the bridge nonlinear vibration frequency and the bridge vehicle coupling frequency, it is judged as bridge-vehicle coupling and vehicle dominant overlap. If none of the above situations apply, an early warning is issued and relevant personnel are notified.

[0048] In this embodiment, the vehicle vibration dominant frequency can be directly obtained through accelerometer data acquisition and fast Fourier transform spectrum analysis. The example steps are as follows: Select a MEMS accelerometer, which is characterized by low cost, small size, and wide bandwidth. It is mounted on the vehicle suspension system or chassis of various test vehicles to ensure that the sensor and the vehicle body vibration transmission path are consistent; Set the sampling frequency fs = 500Hz, covering the dominant frequency range of 10-50Hz, and the sampling time T = 10s. Perform a 5-point moving average on the filtered signal to eliminate random noise. The preprocessed time domain signal is converted into a frequency domain spectrum through fast Fourier transform to directly identify the dominant frequency. Find the frequency point with the largest amplitude in the spectrum, which is the vehicle vibration dominant frequency fvehicle (usually located in the 10-50Hz range). Upload the experimental data sets of various vehicle vibration dominant frequencies to the radio altimeter radar integrated database.

[0049] In the scenario of a cross-sea bridge, three key frequencies are involved:

[0050] Bridge nonlinear vibration frequency (fbridge_nonlinear); bridge-vehicle coupling frequency (fcou ple); vehicle vibration dominant frequency (fvehicle).

[0051] The overlapping scenarios of these three elements can be divided into three categories:

[0052] Low-frequency overlap: fbridge_nonlinear partially overlaps with fvehicle;

[0053] Mid-to-high frequency overlap: fcouple and fvehicle partially overlap;

[0054] Full-band overlap: The frequency ranges of the three overlap significantly.

[0055] Coverlap = Length of overlapping interval of the three frequencies being compared / Maximum coverage length of the three frequencies. Here, Coverlap represents the degree of overlap of the three frequencies. The length of the overlapping interval is the difference between the minimum upper limit and the maximum lower limit of the three frequencies being compared. If the length of the overlapping interval is negative, there is no overlap, and the case of no overlap is not considered. The total frequency band coverage length is the difference between the maximum upper limit and the minimum lower limit of the three frequencies.

[0056] Determining the degree of overlap requires identifying the dominant overlapping frequency pair (i.e., the frequency pair that has the greatest impact on interference), which is determined by comparing the lengths of the overlapping intervals.

[0057] The frequency overlap of the two is calculated as the length of the overlapping interval of the two frequencies being compared, divided by the maximum coverage length of the frequencies of the two frequencies.

[0058] Cbridge-vehicle represents the overlap between the nonlinear vibration frequency of the bridge and the dominant vibration frequency of the vehicle; Ccouple-vehicle represents the overlap between the bridge-vehicle coupling frequency and the dominant vibration frequency of the vehicle; Cbridge-couple represents the overlap between the nonlinear vibration frequency of the bridge and the bridge-vehicle coupling frequency.

[0059] If Cbridge-vehicle > Ccouple-vehicle and Cbridge-vehicle > Cbridge-couple, then the dominant overlap is between the bridge and the vehicle (fbridge_nonlinear and fvehicle).

[0060] If Ccouple-vehicle > Cbridge-vehicle and Ccouple-vehicle > Cbridge-co uple, then the dominant overlap is between the coupling and the vehicle (fcouple and fvehicle);

[0061] If the three overlap to a similar degree, then it is a full-band overlap.

[0062] It should be noted that there are other comparison scenarios, but they almost never correspond to actual situations, and low-probability events are not considered here.

[0063] Furthermore, a second optimization of the interference superposition of the cross-sea bridge height measurement radar is performed, specifically including: if it is determined that the bridge and vehicle are the main overlapping factors, the results of the comparative analysis of the vehicle vibration main frequency and the first safety interval frequency, and the results of the coupling analysis of the bridge nonlinear vibration pseudo-peak frequency and the first safety interval frequency are processed by the maximum value function to obtain the multipath suppression filter cutoff adjustment frequency; the cutoff frequency of the height measurement radar multipath suppression filter is determined according to the multipath suppression filter cutoff adjustment frequency; the multipath suppression filter cutoff frequency is lowered to filter out the low-frequency pseudo-peak of the bridge. The first formula for adjusting the multipath suppression filter cutoff adjustment frequency is: fcutoff_new1=max(fvehicle-2Δf,fbridge_nonlinear+Δf1), where fcutoff_new1 represents the first cutoff adjustment frequency of the multipath suppression filter, and Δf1 is the first safety interval frequency, ensuring that the cutoff frequency is between the bridge pseudo-peak and the vehicle signal. For example, the difference between the bridge pseudo-peak frequency and the vehicle vibration main frequency can be calculated. Subsequently, a quarter of the obtained frequency interval is used to set the first safety interval frequency, which is set by expert prior knowledge. The overlap between the calibration coefficients, the bridge vehicle coupling frequency, and the vehicle vibration main frequency, as well as the coupling result of unit 1, are processed using a logarithmic function to obtain the verification calibration detection value. The original radar detection threshold is compared and analyzed with the verification calibration detection value to obtain the radar calibration detection threshold. The altimeter radar calibration detection threshold is determined based on the radar calibration detection threshold. The threshold is lowered to enhance the low-frequency vehicle signal. The radar calibration detection threshold formula is: Pthreshold_new = Pthreshold_base - 10 * lg(1 + TX * Cbridge - vehicle), where Pthreshold_new represents the radar calibration detection threshold, Pthreshold_base is the original radar detection threshold (i.e., when the signal power exceeds this value, the target is considered to exist); lowering the threshold can increase the detection probability of weak signals, and TX represents the first calibration coefficient of the threshold, set by expert prior knowledge. The lower limit of the radar calibration detection threshold adjustment is directly extracted from the working parameters in the altimeter radar's factory log. The radar calibration detection threshold cannot be lower than the lower limit.

[0064] In this embodiment, if it is determined that the bridge and vehicle vibrations overlap, it means that the spurious peaks of the bridge's nonlinear vibration overlap with the vehicle's vibration signal, causing the vehicle signal to be masked. In this case, the spurious peaks of the bridge's low-frequency vibration are suppressed while the low-frequency characteristics of the vehicle are preserved.

[0065] By filtering out low-frequency spurious peaks on bridges and lowering the detection threshold, the problem of vehicle signals being masked by low-frequency nonlinear interference in fully digital radio altimetry radar has been solved, significantly improving the detection accuracy in low-frequency scenarios.

[0066] In this case, the PRF of the low-frequency overlapping altimeter radar does not need to be adjusted, as the time resolution requirement for low-frequency signals is relatively low.

[0067] Furthermore, the second optimization of interference superposition for cross-sea bridge height measurement radar includes: if it is determined that the bridge-vehicle coupling and vehicle-dominant frequency overlap, the results of the coupling analysis between the vehicle vibration dominant frequency and the first frequency of the safety interval and the results of the comparative analysis between the bridge-vehicle coupling frequency and the second frequency of the safety interval are processed by the minimum value function to obtain the second frequency of multipath suppression filter cutoff adjustment. The cutoff frequency of the height measurement radar multipath suppression filter is determined based on the second frequency of multipath suppression filter cutoff adjustment. The first frequency of height measurement radar pulse repetition frequency adjustment is obtained by using the reference value of height measurement radar pulse repetition frequency, pulse repetition frequency adjustment coefficient, overlap between bridge-vehicle coupling frequency and vehicle vibration dominant frequency and unit coupling analysis. The height measurement radar pulse repetition frequency is adjusted based on the first frequency of height measurement radar pulse repetition frequency adjustment.

[0068] In this embodiment, the coupling frequency and the vehicle's dominant frequency overlap primarily at mid-to-high frequencies, and the coupling frequency overlaps with the vehicle's dominant vibration frequency, causing the vehicle signal to be masked by the coupling spurious peak. The adjustment here is used to separate the coupling spurious peak from the vehicle signal, preserving the vehicle's dominant frequency characteristics.

[0069] Multipath suppression filter cutoff frequency: The cutoff frequency is increased to filter out coupling spurious peaks. The formula for adjusting the second cutoff frequency of the multipath suppression filter is as follows: fcutoff_new1=min(fvehicle+2Δf2,fcouple-Δf2), where fcutoff_new2 represents the second cutoff adjustment frequency of the multipath suppression filter, and Δf2 is the second safety interval frequency, ensuring that the cutoff frequency is located between the bridge vehicle coupling frequency and the vehicle vibration main frequency, which is set by expert prior knowledge;

[0070] To improve time resolution and separate coupling spurious peaks from rapid changes in vehicle signals, the first formula for adjusting the pulse repetition frequency of the altimeter radar is: PRFnew1=PRFbase*(1+TX1*Ccouple-ve hicle), where PRFnew1 represents the first frequency for adjusting the pulse repetition frequency of the altimeter radar, PRFbase represents the reference value of the pulse repetition frequency of the altimeter radar, and Ccouple-vehicle represents the overlap between the bridge vehicle coupling frequency and the vehicle vibration main frequency; the original radar detection threshold remains unchanged, and TX1 represents the pulse repetition frequency adjustment coefficient, which is set by expert prior knowledge.

[0071] Furthermore, the second optimization of interference superposition in the height measurement radar for cross-sea bridges includes: if it is determined to be full-band overlap, then the nonlinear vibration frequency of the bridge, the vehicle coupling frequency of the bridge, and the main vibration frequency of the vehicle are processed by the median function to obtain the third frequency for multipath suppression filter cutoff adjustment; the band-stop filter frequency of the height measurement radar multipath suppression filter is determined based on the second frequency for multipath suppression filter cutoff adjustment; the second adjustment coefficient of the threshold, the frequency overlap of the three and the coupling result of unit 1 are processed by the logarithmic function and recorded as the frequency coupling adjustment coefficient of the three to obtain the first value for verification and calibration detection; the original radar detection threshold is compared and analyzed with the first value for verification and calibration detection to obtain the first threshold for radar calibration detection; the height measurement radar calibration detection threshold is determined based on the first threshold for radar calibration detection; the first frequency for height measurement radar pulse repetition frequency adjustment is obtained by analyzing the reference value of the height measurement radar pulse repetition frequency, the first adjustment coefficient of the pulse repetition frequency, the frequency overlap of the three and the coupling of unit 1; the height measurement radar pulse repetition frequency is adjusted based on the first frequency for height measurement radar pulse repetition frequency adjustment.

[0072] In this embodiment, full-band overlap means high overlap across the three frequency ranges. Bridge spurious peaks, coupling spurious peaks, and vehicle signals are intertwined and difficult to distinguish. It is necessary to comprehensively suppress low-frequency bridge spurious peaks and mid-to-high-frequency coupling spurious peaks while preserving the vehicle's main frequency characteristics. The multipath suppression filter simultaneously suppresses both low-frequency bridge signals (bridge_nonlinear) and mid-to-high-frequency coupling signals (fcouple). The formula for adjusting the third cutoff frequency of the multipath suppression filter is as follows: fcutoff_new3 = median(fbridge_nonlinear, fcouple, fvehicle); fcutoff_new3 represents the third cutoff frequency of the multipath suppression filter, which is the filter frequency set after the multipath suppression filter is adjusted to a band-stop filter.

[0073] To significantly lower the threshold and enhance vehicle signal strength, the constraint formula for the first threshold of radar calibration detection is: Pthreshold_new1 = Pthreshold_base - 10 * lg(1 + TX3 * Coverlap), where Pthreshold_new1 represents the first threshold of radar calibration detection, Coverlap represents the frequency overlap of the three factors, and TX3 represents the second calibration coefficient of the threshold, set by expert prior knowledge. TX3 > TX. The lower limit of the radar calibration detection threshold adjustment is directly extracted from the operating parameters in the altimeter radar's factory log. The radar calibration detection threshold cannot be lowered than the lower limit.

[0074] To significantly improve time resolution and separate overlapping signals, the PRF (Pulse Repetition Frequency) is increased. The formula is: PRFnew2 = PRFBase * (1 + TX2 * Coverlap), where TX2 represents the first coefficient for pulse repetition frequency adjustment, set by expert prior knowledge. TX2 > TX1. PRFnew2 represents the second frequency for adjusting the pulse repetition frequency of the altimeter radar.

[0075] Furthermore, optimized feedback analysis is conducted to generate judgments and early warnings, specifically including:

[0076] After optimization and adjustment, if the final signal-to-noise ratio of the cross-sea bridge height measurement radar is less than the preset effective signal-to-noise ratio threshold, or the final false detection rate of vehicle detection is still greater than the preset false detection rate target value, or the overlap of bridge vehicle coupling frequencies is still greater than the preset safe overlap threshold, the radar system's audible and visual alarm device will be activated and an email will be simultaneously pushed to the maintenance personnel's terminal. The email will include key parameters such as the current signal-to-noise ratio, false detection rate, and overlap. The execution results of all current adjustment steps (such as filter cutoff frequency adjustment value, PRF adjustment value, and threshold adjustment value), environmental parameters (humidity, rainfall, and wind speed), and bridge vehicle coupling parameters (vibration frequency and overlap) will be stored in non-volatile memory for subsequent troubleshooting.

[0077] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0081] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0082] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An optimization method for a fully digital radio altimeter radar based on artificial intelligence, characterized in that, Includes the following steps: The first analysis of the natural environment interference effect of the height measurement radar for cross-sea bridges was conducted, and the first optimization of the interference superposition of the height measurement radar for cross-sea bridges was carried out. A second analysis of the natural environment interference effect of the cross-sea bridge height measurement radar is conducted, and a second optimization of the interference superposition of the cross-sea bridge height measurement radar is performed. Perform optimized feedback analysis and issue warnings; The judgment involves a second analysis of the natural environment interference effects of the cross-sea bridge height measurement radar, specifically including: If the ambient wind speed of the altimeter radar is less than or equal to the ambient wind speed threshold of the altimeter radar, then the second analysis of the natural environment interference effect of the altimeter radar for the cross-sea bridge will not be conducted. If the ambient wind speed of the altimeter exceeds the threshold of the altimeter, a second analysis of the natural environment interference effect of the altimeter on the cross-sea bridge will be conducted. The second analysis of the natural environment interference effects of the altimeter radar for the cross-sea bridge includes: The Strouhal number, characteristic width, and nonlinear stiffness coefficient of the cross-sea bridge were directly extracted from the integrated database of radio altimetry radar. The sea wind speed of the cross-sea bridge was collected by the anemometer at the top of the radar antenna mast. The vibration displacement is measured in real time by an inertial measurement sensor installed at the bottom of the bridge box girder. The amplitude is extracted by fast Fourier transform analysis to obtain the maximum amplitude of bridge vibration. The Strauhal number of the cross-sea bridge is coupled with the sea wind speed of the cross-sea bridge and then compared with the characteristic width of the bridge to obtain the linear vibration frequency of the bridge. By comparing and analyzing the coupling results of the unit and the nonlinear stiffness coefficient of the bridge with the maximum amplitude of the bridge vibration, and then performing square root analysis, the frequency adjustment coefficient of the nonlinear vibration of the bridge is obtained. The nonlinear vibration frequency of the bridge is obtained by coupling analysis of the bridge's linear vibration frequency and the bridge's nonlinear vibration frequency adjustment coefficient.

2. The artificial intelligence-based fully digital radio altimeter radar optimization method as described in claim 1, characterized in that, The first analysis of the natural environment interference effects of the radar for measuring the height of cross-sea bridges specifically includes: The ambient humidity of the altimeter radar is collected using temperature and humidity sensors. The intensity of ambient rainfall is directly obtained from the meteorological database for altimetry radar. The humidity influence coefficient and rainfall influence coefficient were directly extracted from the integrated database of radio altimetry radar. If the ambient humidity of the altimeter radar is greater than the altimeter radar ambient humidity threshold and the ambient rainfall intensity is greater than the altimeter radar ambient rainfall intensity threshold, then the signal-to-noise ratio reduction analysis value of the altimeter radar for the cross-sea bridge can be obtained through comprehensive analysis of the altimeter radar ambient humidity, the altimeter radar ambient rainfall intensity, the humidity influence coefficient, and the rainfall influence coefficient.

3. The artificial intelligence-based fully digital radio altimeter radar optimization method as described in claim 1, characterized in that, The first optimization of radar interference superposition for cross-sea bridge height measurement specifically includes: The baseline signal-to-noise ratio (SNR) of the height measurement radar is directly extracted from the historical database of the radio altimeter radar. By comparing the baseline SNR of the height measurement radar with the SNR decrease analysis value of the cross-sea bridge height measurement radar, the lower limit value of the SNR fluctuation of the cross-sea bridge height measurement radar can be obtained. If the lower limit of the signal-to-noise ratio fluctuation of the cross-sea bridge height measurement radar is greater than the signal-to-noise ratio failure, optimization will not be triggered. If the lower limit of the signal-to-noise ratio fluctuation of the cross-sea bridge height measurement radar is equal to or less than the signal-to-noise ratio failure, optimization will be triggered. The minimum value of the pulse repetition frequency of the altimeter radar, the reference value of the pulse repetition frequency of the altimeter radar, and the maximum signal-to-noise ratio loss threshold are directly extracted from the integrated database of radio altimeter radar. After comparing and analyzing the reference value of the pulse repetition frequency of the altimeter radar with the minimum value of the pulse repetition frequency of the altimeter radar, it is recorded as the adjustment range value of the pulse repetition frequency of the altimeter radar. The results of the ratio analysis of the unit and the lower limit of the signal-to-noise ratio fluctuation of the altimeter radar of the cross-sea bridge with the maximum signal-to-noise ratio loss threshold are compared and analyzed to obtain the signal-to-noise ratio fluctuation loss coefficient of the altimeter radar. By coupling the minimum value of the altimeter radar pulse repetition frequency, the adjustment range value of the altimeter radar pulse repetition frequency, and the signal-to-noise ratio fluctuation loss coefficient of the altimeter radar, the optimized value of the altimeter radar pulse repetition frequency is obtained. The trigger optimization is used to determine the pulse repetition frequency output by the altimeter radar based on the optimized value of the altimeter radar pulse repetition frequency.

4. The artificial intelligence-based fully digital radio altimeter radar optimization method as described in claim 1, characterized in that, The second analysis of the natural environment interference effects of the cross-sea bridge height measurement radar also includes: The bridge mass, bridge stiffness, and fitted damping ratio were directly extracted from the integrated database of radio altimetry radar. The total average mass of vehicles on the bridge is measured in real time by a vehicle weighing system installed at the bridge entrance and exit. After identifying the specific vehicle model, the corresponding vehicle stiffness is directly obtained from the integrated database of radio altimetry radar, and then summed and averaged to obtain the total average stiffness of the vehicle. By fitting the damping ratio and then coupling it with the square root analysis results of the bridge mass and the total average mass of the vehicles, the total coupled damping coefficient of the bridge and vehicles is obtained. After comparing and analyzing the bridge stiffness with the total average stiffness of the vehicles, and then performing a square root analysis by coupling it with the total coupling damping coefficient of the bridge and vehicles, the result is denoted as the total coupling attenuation coefficient of the bridge and vehicles. After coupling analysis of bridge stiffness and vehicle total average stiffness, the results are compared with the bridge-vehicle total coupling attenuation coefficient, and then the coupling results of bridge mass and vehicle total average mass are analyzed to obtain the basic value of bridge-vehicle coupling frequency. The bridge vehicle coupling frequency is obtained by taking the square root of the basic value of the bridge vehicle coupling frequency and then coupling it with a predefined coefficient. Frequency coupling degree is analyzed based on the vehicle vibration dominant frequency, the bridge nonlinear vibration frequency, and the bridge-vehicle coupling frequency.

5. The artificial intelligence-based fully digital radio altimeter radar optimization method as described in claim 4, characterized in that, The analysis of frequency coupling based on the vehicle vibration dominant frequency, the bridge nonlinear vibration frequency, and the bridge-vehicle coupling frequency specifically includes: The vehicle vibration frequency was directly extracted from the integrated database of radio altimetry radar. By comparing the length of the overlap interval between any two frequencies with the maximum coverage length of the frequency coupling degree analysis of the vehicle vibration main frequency, the bridge nonlinear vibration frequency, and the bridge-vehicle coupling frequency, the overlap degree of the bridge nonlinear vibration frequency and the vehicle vibration main frequency, the overlap degree of the bridge-vehicle coupling frequency and the bridge-vehicle coupling frequency is obtained. If the overlap between the nonlinear vibration frequency of the bridge and the dominant vibration frequency of the vehicle is greater than the overlap between the bridge-vehicle coupling frequency and the dominant vibration frequency of the vehicle, and the overlap between the nonlinear vibration frequency of the bridge and the dominant vibration frequency of the vehicle is greater than the overlap between the nonlinear vibration frequency of the bridge and the coupling frequency of the bridge-vehicle, then it is determined that the bridge and vehicle are dominantly overlapping. If the overlap between the bridge-vehicle coupling frequency and the vehicle vibration dominant frequency is greater than the overlap between the bridge nonlinear vibration frequency and the vehicle vibration dominant frequency, and the overlap between the bridge-vehicle coupling frequency and the vehicle vibration dominant frequency is greater than the overlap between the bridge nonlinear vibration frequency and the bridge-vehicle coupling frequency, then it is determined that the bridge-vehicle coupling and the vehicle-dominant frequency overlap. If the overlap between the nonlinear vibration frequency of the bridge and the dominant vibration frequency of the vehicle, the overlap between the bridge-vehicle coupling frequency and the dominant vibration frequency of the vehicle, and the overlap between the nonlinear vibration frequency of the bridge and the coupling frequency of the bridge-vehicle are all greater than the overlap determination threshold, then it is judged as full-band overlap.

6. The artificial intelligence-based fully digital radio altimeter radar optimization method as described in claim 5, characterized in that, The second optimization of radar interference superposition for cross-sea bridge height measurement specifically includes: If it is determined that the main frequency of the bridge and the vehicle are overlapping, the results of the comparative analysis of the vehicle vibration main frequency and the first frequency of the safety interval and the results of the coupling analysis of the bridge nonlinear vibration pseudo-peak frequency and the first frequency of the safety interval are processed by the maximum value function to obtain the first frequency of the multipath suppression filter cutoff adjustment. The cutoff frequency of the multipath suppression filter of the altimeter is determined based on the first frequency of the multipath suppression filter cutoff adjustment. The first calibration coefficient of the threshold, the overlap between the bridge vehicle coupling frequency and the vehicle vibration main frequency, and the coupling result of unit 1 are processed by the logarithmic function to obtain the test calibration detection value. The original radar detection threshold is compared and analyzed with the test calibration detection value to obtain the radar calibration detection threshold. The height measurement radar calibration detection threshold is determined based on the radar calibration detection threshold.

7. The artificial intelligence-based fully digital radio altimeter radar optimization method as described in claim 5, characterized in that, The second optimization of radar interference superposition for cross-sea bridge height measurement also includes: If it is determined that the bridge-vehicle coupling overlaps with the vehicle-dominant frequency, the results of the coupling analysis between the vehicle vibration dominant frequency and the first frequency of the safety interval and the results of the comparison analysis between the bridge-vehicle coupling frequency and the second frequency of the safety interval are processed by the minimum value function to obtain the second frequency of the multipath suppression filter cutoff adjustment. The cutoff frequency of the altimeter multipath suppression filter is determined based on the second frequency of the multipath suppression filter cutoff adjustment. By analyzing the reference value of the altimeter radar pulse repetition frequency, the pulse repetition frequency adjustment coefficient, the overlap between the bridge vehicle coupling frequency and the vehicle vibration main frequency, and the unit coupling, the first frequency for adjusting the altimeter radar pulse repetition frequency is obtained. The altimeter radar pulse repetition frequency is then adjusted according to the first frequency for adjusting the altimeter radar pulse repetition frequency.

8. The artificial intelligence-based fully digital radio altimeter radar optimization method as described in claim 5, characterized in that, The second optimization of radar interference superposition for cross-sea bridge height measurement also includes: If it is determined that there is full-band overlap, the nonlinear vibration frequency of the bridge, the vehicle coupling frequency of the bridge, and the main vibration frequency of the vehicle are processed by the median function to obtain the third frequency of the multipath suppression filter cutoff adjustment. The band-stop filtering frequency of the multipath suppression filter for the altimeter radar is determined by adjusting the second frequency based on the cutoff of the multipath suppression filter. The second calibration coefficient of the threshold, the frequency overlap of the three and the coupling result of the unit are processed by the logarithmic function and denoted as the frequency coupling calibration coefficient of the three. The first value of the calibration test is obtained. The original radar detection threshold is compared and analyzed with the first value of the calibration test to obtain the first threshold of radar calibration test. The calibration test threshold of the altimeter radar is determined based on the first threshold of radar calibration test. By analyzing the reference value of the altimeter radar pulse repetition frequency, the first coefficient of the pulse repetition frequency adjustment, the frequency overlap of the three, and the unit coupling, the first frequency for adjusting the altimeter radar pulse repetition frequency is obtained, and the altimeter radar pulse repetition frequency is adjusted according to the first frequency for adjusting the altimeter radar pulse repetition frequency.

9. The artificial intelligence-based fully digital radio altimeter radar optimization method as described in claim 1, characterized in that, The optimized feedback analysis and judgment / early warning process specifically includes: After optimization and adjustment, if the final signal-to-noise ratio of the cross-sea bridge height measurement radar is less than the preset effective signal-to-noise ratio threshold, or the final false detection rate of vehicle detection is still greater than the preset false detection rate target value, or the overlap of bridge vehicle coupling frequencies is still greater than the preset safe overlap threshold, then the radar system's audible and visual alarm device will be activated, and an email will be simultaneously pushed to the maintenance personnel's terminal. The execution results of all current adjustment steps, environmental parameters, and bridge vehicle coupling parameters will be stored in non-volatile memory.

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