Self-adaptive denoising method and system for on-line radar measuring point displacement
By combining phase vector analysis and dynamic signal-to-noise ratio estimation with extended Kalman filtering technology, the problem of inconsistent signal-to-noise ratio when measuring target displacement at different distances by online radar is solved, achieving adaptive denoising, improving measurement accuracy and stability, and making it suitable for monitoring important infrastructure such as bridges.
Patent Information
- Application Number
- CN202511817559.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-04
- Publication Date
- 2026-02-13
AI Technical Summary
Online radars exhibit inconsistent signal-to-noise ratios (SNRs) for target displacement measurements at different distances, and suffer from issues such as frequency ramp nonlinearity, phase noise, SNR variations, and clutter interference, which affect measurement accuracy and stability.
By combining phase vector analysis and dynamic signal-to-noise ratio estimation with extended Kalman filtering, adaptive denoising is achieved at different distances through Fourier transform, circle fitting algorithm, and extended Kalman filtering algorithm.
It improves the measurement accuracy and robustness of radar systems in low signal-to-noise ratio and complex environments, reduces computational complexity, ensures the accuracy and reliability of long-term continuous monitoring, and is suitable for monitoring large-scale complex structures.
Smart Images
Figure CN121522578A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of microwave radar displacement measurement, in particular to an online radar measurement point displacement adaptive denoising method and system. BACKGROUND
[0002] Microwave interferometry is one of the core principles of radar displacement and vibration monitoring, has significant advantages, and is particularly suitable for bridge health monitoring applications. This technology realizes long-distance accurate displacement measurement by directly extracting phase information, and is very suitable for monitoring large flexible structures (such as bridges) and micro-vibration sensing and other special applications (such as sensing in medical and rescue operations). Compared with traditional displacement measurement methods (such as leveling and displacement meters), radar systems have many advantages, but in different distance multi-target scenarios, radar systems still face challenges in high-precision displacement measurement, such as frequency slope nonlinearity, phase noise, signal-to-noise ratio change, and clutter interference.
[0003] Although phase-based measurement methods such as approximate ML algorithm (Maximum Likelihood algorithm) have achieved sub-millimeter accuracy under experimental conditions, there are still certain limitations due to noise and multi-target interference. The phasor offset compensation technology combined with the hybrid radar system of frequency-modulated continuous wave and interferometry mode has shown potential in vibration monitoring, but the problems of inconsistent signal-to-noise ratio between targets, spectrum leakage, and clutter interference have not been solved. Therefore, an adaptive denoising method needs to be developed.
[0004] A variety of denoising methods have been applied to radar and displacement signal processing, traditional frequency domain methods such as Fourier transform and wavelet transform, but Fourier transform is only suitable for stationary signals, while wavelet transform has advantages in time-frequency positioning, but is sensitive to wavelet basis and threshold selection, which may cause time domain distortion and loss of key information. For non-stationary and nonlinear signals, ensemble empirical mode decomposition has been proposed, but when the signal and noise frequency bands overlap, performance degradation and signal loss problems may still occur. SUMMARY
[0005] The purpose of the present application is to provide an online radar measurement point displacement adaptive denoising method and system, which solves the problem of inconsistent signal-to-noise ratio of measurement target displacement at different distances by phase vector analysis and dynamic signal-to-noise ratio estimation, combined with extended Kalman filter technology, and realizes adaptive denoising of displacement signals of measurement targets at different distances.
[0006] To achieve the above purpose, the present application provides an online radar measurement point displacement adaptive denoising method, comprising the following steps: S1, according to the online displacement measurement radar and the measurement target, the difference frequency signal is obtained; S2. Based on the difference frequency signal, determine the slow beat difference frequency signal of the measurement target through Fourier transform; S3. Use the circle fitting algorithm to perform phase vector analysis on the slow beat frequency signal to obtain the fitted circle center and phase amplitude estimate; S4. Calculate the signal-to-noise ratio of the slow beat frequency signal based on the slow beat frequency signal, the fitted circle center, and the estimated phase amplitude. S5. Based on the signal-to-noise ratio, the slow beat frequency signal is adaptively denoised using the extended Kalman filter algorithm to obtain the filtered phase signal. S6. Based on the phase interferometry method, the filtered phase signal is converted into the displacement signal of the target to be measured.
[0007] Preferably, the specific content of obtaining the difference frequency signal based on the online displacement radar and the measurement target in S1 includes: The online displacement radar transmits microwave signals to the target being measured, and the microwave signals are reflected by the target to obtain the echo signal; The online displacement radar receives the echo signal and performs amplification, down-conversion, and filtering processes on the echo signal to obtain the difference frequency signal.
[0008] Preferably, the expression for the difference frequency signal is: ; in, It is a difference frequency signal. The amplitude of the difference frequency signal. Pi j The imaginary unit, The phase of the difference frequency signal. The frequency of the difference frequency signal. t For time, Noise signals caused by clutter or thermal noise. For dimension m × n The matrix, m The number of sampling points in a single snapshot. n The number of snapshots taken.
[0009] Preferably, in step S2, the specific content of determining the slow-beat difference frequency signal of the measurement target through Fourier transform based on the difference frequency signal includes: Perform a Fourier transform on the difference frequency signal to obtain the range spectrum of the target. Obtain the peak points of the distance spectrum; Based on the peak point, determine the slow-beat difference frequency signal of the measurement target.
[0010] Preferably, in S3, the circle fitting algorithm is used to perform phase vector analysis on the slow-beat difference frequency signal to obtain the specific contents of the fitted circle center and phase amplitude estimate, including: The slow-motion difference frequency signal is represented as a set of data points on the complex plane; The data point set is fitted using a circle fitting algorithm to obtain the center of the fitted circle; Phase amplitude estimation is performed based on the slow-motion difference frequency signal, the fitted circle center, and the origin of the complex plane to obtain the estimated phase amplitude value.
[0011] Preferably, the expression for the phase amplitude estimate is: ; in, This is the estimated value of the phase amplitude. For the first i A difference frequency signal, The phase rotation factor, j The imaginary unit, The phase angle, i For quantity, i =1,2,…, n , n It is a positive integer.
[0012] Preferably, the specific steps in S4 for calculating the signal-to-noise ratio of the slow-beat difference frequency signal based on the slow-beat difference frequency signal, the fitted circle center, and the estimated phase amplitude include: Calculate the fitting radius based on the data point set and the center of the fitting circle, and then obtain the average value of the fitting radius. Calculate the noise amplitude corresponding to the slow-beat difference frequency signal based on the fitting radius and the average fitting radius; The signal-to-noise ratio of the slow-beat difference frequency signal is calculated based on the estimated phase amplitude and noise amplitude.
[0013] Preferably, the expression for the fitting radius is: ; in, d i The fitting radius is... i For the first i One sampling point, x i For the first i The x-coordinate of each sampling point y i For the first i The ordinate of each sampling point i For quantity, x c To fit the x-coordinate of the circle's center, y c The ordinate of the fitted circle's center; The expression for the noise amplitude is: ; in, For noise amplitude, The average value of the fitted radius; The expression for the signal-to-noise ratio is: ; in, This refers to the signal-to-noise ratio.
[0014] Preferably, in step S5, based on the signal-to-noise ratio, the slow-beat difference frequency signal is adaptively denoised using an extended Kalman filter algorithm to obtain the specific content of the filtered phase signal, including: Based on the signal-to-noise ratio, calculate the noise covariance and construct the noise covariance matrix; Based on the noise covariance matrix, the slow-beat difference frequency signal is adaptively denoised using the extended Kalman filter algorithm to obtain the filtered phase signal.
[0015] The present invention also provides an adaptive noise reduction system for online radar measurement point displacement, comprising: The difference frequency signal acquisition module is used to acquire the difference frequency signal based on the online displacement radar and the target being measured. The slow-beat difference frequency signal determination module is used to determine the slow-beat difference frequency signal of the measurement target based on the difference frequency signal through Fourier transform. The phase vector analysis module is used to perform phase vector analysis on slow beat frequency signals using a circle fitting algorithm to obtain the fitted circle center and phase amplitude estimates. The signal-to-noise ratio (SNR) calculation module is used to calculate the SNR of the slow-beat difference frequency signal based on the slow-beat difference frequency signal, the fitted circle center, and the estimated phase amplitude. The adaptive denoising module is used to adaptively denoise the slow-beat difference frequency signal according to the signal-to-noise ratio using the extended Kalman filter algorithm to obtain the filtered phase signal. The signal conversion module is used to convert the filtered phase signal into the displacement signal of the target being measured based on the phase interferometry method.
[0016] In summary, the adaptive denoising method and system for online radar displacement measurement of the present invention, compared with traditional technologies, has the following advantages: By combining phase vector analysis and dynamic signal-to-noise ratio estimation with extended Kalman filtering technology, the problem of inconsistent signal-to-noise ratio of measured target displacement caused by different target distances in online radar measurement is effectively solved, realizing adaptive denoising of displacement signals of targets measured at different distances, and ensuring the stability and accuracy of measurement; the extended Kalman filtering algorithm can adjust the filtering strategy in real time, improving the measurement accuracy and robustness of the radar system in low signal-to-noise ratio, multi-target, and complex environments, making it suitable for large-scale, complex structure monitoring, reducing computational complexity, improving the real-time performance of the denoising process, and ensuring that the radar system provides accurate and reliable displacement data in long-term, continuous monitoring, which is of great significance, especially for monitoring important infrastructure such as bridges.
[0017] The technical method of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description
[0018] Figure 1 This is a flowchart of an adaptive noise reduction method for online radar measurement point displacement according to the present invention; Figure 2 This is a distance spectrum of the target measured in an embodiment of the present invention; Figure 3 The diagram shows the displacement signal results of five measurement targets in an embodiment of the present invention; wherein, Figure 3 (a) in the figure shows the displacement signal result of target 1. Figure 3 (b) in the figure shows the displacement signal results of target 2. Figure 3 (c) in the figure shows the displacement signal results of target 3. Figure 3 (d) in the figure shows the displacement signal results of target 4. Figure 3 (e) in the figure shows the displacement signal results of target 5. Figure 4 This is a diagram showing the phase vector analysis results of measurement target 3 according to an embodiment of the present invention; wherein, Figure 4 (a) in the figure is the phasor representation diagram of the slow-beat difference frequency signal of the target 3. Figure 4 (b) in the figure is a representation of the fitted circle. Figure 4 (c) is the phasor diagram of the measurement target 3; Figure 5 This is a diagram showing the displacement results of target 3 as described in an embodiment of the present invention. Figure 6 This is a framework diagram of an adaptive noise reduction system for online radar measurement point displacement according to the present invention. Detailed Implementation
[0019] The technical method of the present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values of the components and steps described in these embodiments do not limit the scope of this application.
[0020] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this application and its application or use.
[0021] Techniques, systems, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the instruction manual.
[0022] In all the examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.
[0023] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.
[0024] This invention provides an adaptive denoising method for online radar measurement point displacement, such as... Figure 1 As shown, it includes: Step S1: Obtain the difference frequency signal based on the online displacement radar and the target being measured.
[0025] Furthermore, step S1 can be replaced by steps S101 to S102: Step S101: The online displacement radar transmits microwave signals to the target being measured. The microwave signals are reflected by the target to obtain echo signals.
[0026] Step S102: The online displacement radar receives the echo signal and sequentially amplifies, down-converts, and filters the echo signal to obtain the difference frequency signal. The expression for the difference frequency signal is: ; in, It is a difference frequency signal. The amplitude of the difference frequency signal. Pi j The imaginary unit, The phase of the difference frequency signal. The frequency of the difference frequency signal. t For time, Noise signals caused by clutter or thermal noise. For dimension m × n The matrix, mThe number of sampling points in a single snapshot. n The number of snapshots taken.
[0027] Step S2: Based on the difference frequency signal, determine the slow beat difference frequency signal of the measurement target through Fourier transform.
[0028] Furthermore, step S2 specifically includes the following: Step S201: Perform a Fourier transform on the difference frequency signal to obtain the range spectrum of the target. It can be understood that performing a Fourier transform on the difference frequency signal can be done on any column of the difference frequency signal.
[0029] Step S202: Obtain the peak points of the distance spectrum.
[0030] Step S203: Determine the slow beat difference frequency signal of the measurement target based on the peak point.
[0031] Step S3: Use the circle fitting algorithm to perform phase vector analysis on the slow beat frequency signal to obtain the fitted circle center and phase amplitude estimate.
[0032] The distance from the center of the fitted circle to the origin of the complex plane is the offset of the fitted circle center relative to the origin of the complex plane, i.e., the deviation vector.
[0033] Furthermore, step S3 specifically includes: Step S301: Represent the slow-motion difference frequency signal as a set of data points on the complex plane.
[0034] Step S302: Use a circle fitting algorithm to fit the data point set to obtain the center of the fitted circle.
[0035] Step S303: Based on the slow-motion difference frequency signal, the fitted circle center, and the origin of the complex plane, perform phase amplitude estimation to obtain the phase amplitude estimate. The expression for the phase amplitude estimate is: ; in, This is the estimated value of the phase amplitude. For the first i A difference frequency signal, The phase rotation factor, The phase angle, i For quantity, i =1,2,…, n , n It is a positive integer.
[0036] Step S4: Calculate the signal-to-noise ratio of the slow beat frequency signal based on the slow beat difference frequency signal, the fitted circle center, and the estimated phase amplitude.
[0037] Furthermore, the specific content of step S4 includes: Step S401: Calculate the fitting radius based on the data point set and the center of the fitting circle, and then calculate the average fitting radius. The expression for the fitting radius is: ; in, d i The fitting radius is... x i For the first i The x-coordinate of each sampling point y i For the first i The ordinate of each sampling point x c To fit the x-coordinate of the circle's center, y c The ordinate is the ordinate of the fitted circle center.
[0038] Step S402: Calculate the noise amplitude corresponding to the slow-motion difference frequency signal based on the fitting radius and the average fitting radius. The expression for the noise amplitude is: ; in, For noise amplitude, This represents the average of the fitted radii.
[0039] Step S403: Calculate the signal-to-noise ratio (SNR) of the slow-motion difference frequency signal based on the estimated phase amplitude and noise amplitude. The expression for the SNR is: ; in, This refers to the signal-to-noise ratio.
[0040] Step S5: Based on the signal-to-noise ratio, the slow-beat difference frequency signal is adaptively denoised using the extended Kalman filter algorithm to obtain the filtered phase signal.
[0041] Furthermore, the specific content of step S5 includes: Step S501: Calculate the noise covariance based on the signal-to-noise ratio and construct the noise covariance matrix.
[0042] Step S502: Based on the noise covariance matrix, the slow beat frequency signal is adaptively denoised using the extended Kalman filter algorithm to obtain the filtered phase signal.
[0043] The phase signal of the measured target changes smoothly within the slow frame, and its rate of change is approximately constant. The corresponding state vector, i.e., the filtered phase signal, is represented as: ; in, For status signals, It is a phase signal. The phase change rate, Let be the rate of change of the state vector over time.
[0044] Discrete state of phase signal after discretization Represented as: ; in, Here is the state transition matrix. , For slow-motion sampling intervals, For the first k The state vector at each time step; For process noise that follows a Gaussian distribution, its noise covariance is: .
[0045] The corresponding measurement equation is: ; in, For the first k Phase signal observed at each time step For the measurement matrix, To measure noise, For the first k The phase observed at each time step For the first k The rate of phase change observed at each time step.
[0046] Step S6: Based on the phase interferometry method, convert the filtered phase signal into the displacement signal of the target to be measured.
[0047] Based on the adaptive denoising method for online radar measurement point displacement proposed in this invention, a multi-target sliding stage experiment was designed and implemented. In this experiment, five aluminum corner reflectors (250 mm × 250 mm × 250 mm) were used as measurement targets (target 1, target 2, target 3, target 4, and target 5), and they were mounted on a tripod. The five targets were arranged along a horizontal line with equal spacing between each target. A Linear Frequency Modulated Continuous Wave (LFMCW) radar using tilt measurement technology was employed. The distance between the radar and the target line was 4.5 m, and the target distance from the radar's effective measurement point was 10 m. The targets remained fixed, while the radar was mounted on a servo-driven stepper motor sliding stage with an accuracy of 0.01 mm. This sliding stage was controlled by a dedicated controller, enabling the radar to move back and forth in a direction perpendicular to the target line. The stepper motor's step size was 0.5 mm, and a total of 10 steps were completed.
[0048] The experiment obtained the range spectra of five measurement targets, such as Figure 2 As shown, five distinct peaks appear in the distance spectrum, each corresponding to a measured target, and the peak height varies depending on the distance to each measured target.
[0049] The displacement signals of the five measurement targets obtained based on the phase interferometry method are as follows: Figure 3 As shown, Figure 3 (a) in the figure shows the displacement signal result of target 1. Figure 3 (b) in the figure shows the displacement signal results of target 2. Figure 3 (c) in the figure shows the displacement signal results of target 3. Figure 3 (d) in the figure shows the displacement signal results of target 4. Figure 3 (e) in the figure shows the displacement signal results of target 5. From... Figure 3 As can be seen, the measurement accuracy of displacement obtained based on the phase interferometry method decreases as the energy of the echo from the target decreases. This trend is particularly pronounced as the wave propagation distance increases. This leads to an increase in the average value and displacement error due to the attenuation of electromagnetic wave power.
[0050] Taking target 3 as an example, phase vector analysis was used to analyze the signal-to-noise ratio of the target. Figure 4 As shown. Figure 4 (a) shows the phasor representation of the slow-beat difference frequency signal of target 3, which exhibits an arc-shaped distribution within the slow beat. A circle is fitted based on the slow-beat difference frequency signal, and the fitted circle is as follows: Figure 4 As shown in (b) of the figure. The center coordinates of the fitted circle are (4.97, 0.35). After removing static clutter, Figure 4 (c) gives the phasor of the measurement target 3, with coordinates (0,0). From this, the signal amplitude can be determined and the noise amplitude can be calculated, resulting in a signal-to-noise ratio of 10.33 dB for the measurement target 3.
[0051] The displacement measurement of target 3, after being processed by extended Kalman filtering, is as follows: Figure 5 As shown, after adaptive filtering, the displacement measurement significantly reduces fluctuations in the stable section of the stepping slide table.
[0052] This invention provides an adaptive noise reduction system for online radar measurement point displacement, such as... Figure 6 As shown, it includes: The difference frequency signal acquisition module is used to acquire the difference frequency signal based on the online displacement radar and the target being measured.
[0053] The slow-beat difference frequency signal determination module is used to determine the slow-beat difference frequency signal of the measurement target based on the difference frequency signal through Fourier transform.
[0054] The phase vector analysis module is used to perform phase vector analysis on slow beat frequency signals using a circle fitting algorithm to obtain the fitted circle center and phase amplitude estimates.
[0055] The signal-to-noise ratio (SNR) calculation module is used to calculate the SNR of the slow-beat difference frequency signal based on the slow-beat difference frequency signal, the fitted circle center, and the estimated phase amplitude.
[0056] The adaptive denoising module is used to adaptively denoise the slow-beat difference frequency signal based on the signal-to-noise ratio using the extended Kalman filter algorithm, thereby obtaining the filtered phase signal.
[0057] The signal conversion module is used to convert the filtered phase signal into the displacement signal of the target being measured based on the phase interferometry method.
[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical methods of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical methods of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical methods to deviate from the spirit and scope of the technical methods of the present invention.
Claims
1. An adaptive denoising method for online radar measurement point displacement, characterized in that, The adaptive denoising method for online radar measurement point displacement includes the following steps: S1. Obtain the difference frequency signal based on the online displacement radar and the target being measured; S2. Based on the difference frequency signal, determine the slow beat difference frequency signal of the measurement target through Fourier transform; S3. Use the circle fitting algorithm to perform phase vector analysis on the slow beat frequency signal to obtain the fitted circle center and phase amplitude estimate; S4. Calculate the signal-to-noise ratio of the slow beat frequency signal based on the slow beat frequency signal, the fitted circle center, and the estimated phase amplitude. S5. Based on the signal-to-noise ratio, the slow beat frequency signal is adaptively denoised using the extended Kalman filter algorithm to obtain the filtered phase signal. S6. Based on the phase interferometry method, the filtered phase signal is converted into the displacement signal of the target to be measured.
2. The adaptive denoising method for online radar measurement point displacement according to claim 1, characterized in that, The specific details of obtaining the difference frequency signal based on the online displacement radar and the measured target in S1 include: The online displacement radar transmits microwave signals to the target being measured, and the microwave signals are reflected by the target to obtain the echo signal; The online displacement radar receives the echo signal and performs amplification, down-conversion, and filtering processes on the echo signal to obtain the difference frequency signal.
3. The adaptive denoising method for online radar measurement point displacement according to claim 2, characterized in that, The expression for the difference frequency signal is: ； in, It is a difference frequency signal. The amplitude of the difference frequency signal. Pi j The imaginary unit, The phase of the difference frequency signal. The frequency of the difference frequency signal. t For time, Noise signals caused by clutter or thermal noise. For dimension m × n The matrix, m The number of sampling points in a single snapshot. n The number of snapshots taken.
4. The adaptive denoising method for online radar measurement point displacement according to claim 1, characterized in that, The specific details of determining the slow-beat difference frequency signal of the measurement target based on the difference frequency signal through Fourier transform in S2 include: Perform a Fourier transform on the difference frequency signal to obtain the range spectrum of the target. Obtain the peak points of the distance spectrum; Based on the peak point, determine the slow-beat difference frequency signal of the measurement target.
5. The adaptive denoising method for online radar measurement point displacement according to claim 1, characterized in that, S3 uses a circle fitting algorithm to perform phase vector analysis on the slow-beat difference frequency signal, and obtains the specific contents of the fitted circle center and phase amplitude estimate, including: The slow-motion difference frequency signal is represented as a set of data points on the complex plane; The data point set is fitted using a circle fitting algorithm to obtain the center of the fitted circle; Phase amplitude estimation is performed based on the slow-motion difference frequency signal, the fitted circle center, and the origin of the complex plane to obtain the estimated phase amplitude value.
6. The adaptive denoising method for online radar measurement point displacement according to claim 5, characterized in that, The expression for the phase amplitude estimate is: ； in, This is the estimated value of the phase amplitude. For the first i A difference frequency signal, The phase rotation factor, j The imaginary unit, The phase angle, i For quantity, i =1,2,…, n , n It is a positive integer.
7. The adaptive denoising method for online radar measurement point displacement according to claim 4, characterized in that, The specific steps in S4 for calculating the signal-to-noise ratio of the slow-beat difference frequency signal based on the slow-beat difference frequency signal, the fitted circle center, and the estimated phase amplitude include: Calculate the fitting radius based on the data point set and the center of the fitting circle, and then obtain the average value of the fitting radius. Calculate the noise amplitude corresponding to the slow-beat difference frequency signal based on the fitting radius and the average fitting radius; The signal-to-noise ratio of the slow-beat difference frequency signal is calculated based on the estimated phase amplitude and noise amplitude.
8. The adaptive denoising method for online radar measurement point displacement according to claim 7, characterized in that, The expression for the fitting radius is: ； in, d i The fitting radius is... x i For the first i The x-coordinate of each sampling point y i For the first i The ordinate of each sampling point i For quantity, x c To fit the x-coordinate of the circle's center, y c The ordinate of the fitted circle's center; The expression for the noise amplitude is: ； in, For noise amplitude, The average value of the fitted radius; The expression for the signal-to-noise ratio is: ； in, This refers to the signal-to-noise ratio.
9. The adaptive denoising method for online radar measurement point displacement according to claim 1, characterized in that, In S5, based on the signal-to-noise ratio, the slow-beat difference frequency signal is adaptively denoised using an extended Kalman filter algorithm. The specific contents of the filtered phase signal include: Based on the signal-to-noise ratio, calculate the noise covariance and construct the noise covariance matrix; Based on the noise covariance matrix, the slow-beat difference frequency signal is adaptively denoised using the extended Kalman filter algorithm to obtain the filtered phase signal.
10. An adaptive noise reduction system for online radar measurement point displacement, characterized in that, include: The difference frequency signal acquisition module is used to acquire the difference frequency signal based on the online displacement radar and the target being measured. The slow-beat difference frequency signal determination module is used to determine the slow-beat difference frequency signal of the measurement target based on the difference frequency signal through Fourier transform. The phase vector analysis module is used to perform phase vector analysis on slow beat frequency signals using a circle fitting algorithm to obtain the fitted circle center and phase amplitude estimates. The signal-to-noise ratio (SNR) calculation module is used to calculate the SNR of the slow-beat difference frequency signal based on the slow-beat difference frequency signal, the fitted circle center, and the estimated phase amplitude. The adaptive denoising module is used to adaptively denoise the slow-beat difference frequency signal according to the signal-to-noise ratio using the extended Kalman filter algorithm to obtain the filtered phase signal. The signal conversion module is used to convert the filtered phase signal into the displacement signal of the target being measured based on the phase interferometry method.