Radar system and method for judging still water state

Abstract

The invention provides a radar system and method for still water state judgment, and the system comprises a coherent step-frequency continuous wave radar subsystem which is used for transmitting a step-frequency continuous wave signal to the water surface and receiving echoes reflected by the water surface, and works in a Ku wave band; and the data processing and control work station is used for processing the echoes by using an interference technology to obtain phase change of a reflected signal of a radar irradiation area and converting the measured phase change into deformation so as to monitor the change of the water surface. The device has the advantages that the device has submillimeter-level high measurement precision, and a non-contact measurement mode can effectively ensure that a worker obtains water surface change information within a safe range; the device is simple in structure, flexible in installation, short in observation period and low in cost, and effectively improves the efficiency of water surface change monitoring and subsequent engineering implementation.

Description

Technical Field

[0001] The present application belongs to the technical field of static water state judgment, and specifically relates to a radar system and method for static water state judgment. Background Art

[0002] The judgment of static water state usually refers to the state in which the flow velocity at a certain point in the water body is zero and there is no obvious vertical flow when the water flow is stable and there are no hydrological changes such as sudden rise and fall. It can be judged by naked eye observation, stationary point measurement of current meter, sonar measurement, radar measurement, etc. The judgment of static water state has a wide range of applications in many fields. In water conservancy projects, static water state judgment plays an important role in the design of dams, reservoirs and other water conservancy facilities. It can be used to analyze water pressure, calculate hydrostatic pressure, and ensure the safety and stability of the structure; in environmental monitoring, the static water state of the water body helps to assess the ecological health of the water body and determine whether there is a source of pollution; in shipping and port management, the static water state affects the berthing and navigation safety of ships, and is used to optimize the layout of berths and waterways; the static water state also has important applications in the design of irrigation systems, which can help design reasonable water flow distribution and ensure that crops have sufficient water sources. The judgment of static water state not only helps experts conduct scientific research and engineering design, but also has a certain impact on people's lives and safety.

[0003] The slight changes in the water surface cannot be judged by the naked eye, and the stationary measurement of the flow meter requires a contact water meter, which poses certain challenges for hard-to-reach areas. Sonar has certain advantages in measuring changes in the water surface. Ultrasonic sensors can be installed at the water's edge without the need for equipment to enter the water directly. Synthetic aperture sonar (SAS) technology can perform fine imaging of the water surface and monitor changes in the water surface state through image processing technology. It has high accuracy, real-time and long-range detection capabilities, but the processing and analysis of sonar data may be more complex and require professional knowledge and software support. It also faces challenges such as environmental dependence and equipment costs. Summary of the invention

[0004] The purpose of the present application is to overcome the defects in the prior art that the installation is difficult and contact measurement is required.

[0005] In order to achieve the above object, the present application proposes a radar system for judging the static water state, the system comprising:

[0006] A coherent stepped-frequency continuous-wave radar subsystem for transmitting stepped-frequency continuous-wave signals to the water surface and receiving echoes reflected from the water surface, operating in the Ku band; and

[0007] The data processing and control workstation is used to process the echoes using interference technology to obtain the phase change of the reflected signal in the radar illumination area, and convert the measured phase change into deformation to realize the monitoring of water surface changes;

[0008] The working process of the data processing and control workstation includes: firstly extracting the phase of the echo signal at different time periods, interfering and filtering after removing the flat ground effect; then obtaining the phase true value of the time period through phase unwrapping, converting the phase change into deformation, and then obtaining the time series change information of the wave height over a period of time when the water surface is becoming calm; finally, judging the still water state of the water surface according to the wave height of the water surface.

[0009] As an improvement of the above system, the working process of the data processing and control workstation includes:

[0010] Step 1: Extract the phase of each observation pulse data in chronological order. When the water surface tends to be calm, observe continuously and obtain the phases at three moments.

[0011] Step 2: Conjugate multiply the two radar images acquired at the previous and next moments to form a phase interference diagram;

[0012] Step 3: Remove the flat ground effect from the phase interferogram based on the frequency shift method;

[0013] Step 4: Perform phase filtering on the phase interference pattern after removing the flat ground effect;

[0014] Step 5: Perform phase unwrapping processing on the phase interference image after phase filtering to obtain accurate phase information corresponding to the deformation information;

[0015] Step 6: The change in water surface wave height is obtained based on the phase difference between the two target echoes. When the wave height change is less than the set threshold, it is determined to be a still water state.

[0016] As an improvement of the above system, step 3 includes:

[0017] Step 3-1: Perform discrete Fourier transform on each row of the phase interference pattern, and superimpose the corresponding elements of the transformation results of each row to obtain the result of spectrum superposition.

[0018]

[0019] Where M represents the number of rows of the phase interferogram; FFT represents discrete Fourier transform; x i (n) is an n-dimensional row vector, which is used to store the n pixel values ​​of the i-th row of the phase interference pattern. n is the number of pixels in the range direction. The result of spectrum superposition is It is also an n-dimensional row vector, and a k-dimensional unit row vector, that is, k = n, and the digital frequency corresponding to its maximum value is the required peak frequency in the distance direction;

[0020] Step 3-2: For x i The Fourier transform of (n) is Xi (k) Circular displacement with frequency:

[0021] Y i (k) = X i (kl) N R N (k)

[0022] in,() N Represents the remainder of N, R N (k) is a rectangular window of length N; N represents the number of columns of the phase interference pattern;

[0023] Step 3-3: For Y i (k) Perform inverse Fourier transform.

[0024] As an improvement of the above system, step 4 includes:

[0025] Step 4-1: Read the image to be filtered;

[0026] Step 4-2: For each pixel in the image, calculate the mean and variance of its local neighborhood through a sliding window;

[0027] Step 4-3: Apply Lee filter to each pixel based on the mean and variance to achieve phase filtering.

[0028] As an improvement of the above system, step 5 includes:

[0029] Step 5-1: Calculate the two-dimensional phase gradient of the phase interference pattern respectively, denoted as ρ;

[0030] Step 5-2: Mirror the two-dimensional phase gradient with the upper and right boundaries of the image as the symmetry axis to obtain an area four times the size of the original phase interference pattern, recorded as

[0031] Step 5-3: Perform discrete Fourier transform, denoted as P(m, n); the value ranges of m and n are [0, M] and [0, N], respectively, and M and N represent the number of rows and columns in the interference phase image, respectively;

[0032] Step 5-4: According to the mirror transformation process and the properties of the two-bit discrete Fourier transform, the unwrapped phase Φ(m, n) is obtained:

[0033]

[0034] Among them, setting

[0035] Step 5-5: Perform inverse Fourier transform on φ(m, n), and take the first (M+1) rows and the first (N+1) columns to achieve phase unwrapping.

[0036] As an improvement of the above system, step 6 includes:

[0037]

[0038] Where ΔR represents the change in water surface wave height; △φ represents the phase difference between two target echoes; c represents the propagation speed of radar waves; f c Indicates the center frequency of the signal collected by the radar system.

[0039] The present application also provides a method for judging the static water state, which is implemented based on the above system, and the method includes:

[0040] The coherent stepped frequency continuous wave radar subsystem transmits a stepped frequency continuous wave signal to the water surface and receives the echo reflected from the water surface;

[0041] The data processing and control workstation uses interference technology to process the echo to obtain the phase change of the reflected signal in the radar illumination area, and converts the measured phase change into deformation to monitor the changes in the water surface.

[0042] Compared with the prior art, the advantages of this application are:

[0043] 1. The present invention proposes a radar system and method for judging the static water state. The technical solution is selected according to the characteristics of the static water state, and the SFCW system is combined with the interference technology. The SFCW radar has the advantages of small size, light weight, and low cost, and has a high measurement accuracy of sub-millimeter level. The non-contact measurement method can also effectively ensure that the staff can obtain the water surface change information within a safe range, and provide a non-contact measurement method with flexible installation, short observation period and low cost for the judgment and monitoring of the static water state, which effectively improves the efficiency of water surface change monitoring and subsequent engineering implementation;

[0044] 2. The present invention adopts Ku-band radar static observation technology to target the scattering characteristics of the water surface. The antenna uses a separate transmitting and receiving antenna to transmit SFCW signals and continuously collect radar echo signals to achieve real-time monitoring of water surface changes.

[0045] 3. The method and system proposed in the present invention can adjust the radar's viewing angle, scanning length, and signal bandwidth according to the distance between the observation position and the water surface and the viewing angle, making it easy to set up. The present invention supports synchronous observation with optical equipment, and the fusion of multi-source data facilitates the monitoring of regional water meter changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The following is a block diagram of the working principle of the radar system used for judging and monitoring the static water state;

[0047] Figure 2The figure shows the overall flow structure block diagram of the radar system;

[0048] FIG3( a ) shows a phase diagram obtained at time 1 of continuous radar measurement for water surface status monitoring;

[0049] FIG3( b ) shows a phase diagram obtained at time 2 of continuous radar measurement for water surface status monitoring;

[0050] FIG3( c ) shows a phase diagram obtained at time 3 of continuous radar measurement for water surface status monitoring;

[0051] Figure 4(a) shows the phase diagram of the interference at time 2 and time 1;

[0052] Figure 4(b) shows the phase diagram of the interference at time 3 and time 2;

[0053] FIG5( a ) shows a deflating effect diagram for the phase interference diagram at time 2 and time 1;

[0054] FIG5( b ) shows the deflating effect diagram for the phase interference diagram at time 3 and time 2;

[0055] FIG6( a ) shows a phase filtering diagram for removing the flat ground effect diagram at time 2 and time 1;

[0056] FIG6( b ) shows a phase filtering diagram for removing the flat ground effect diagram at time 3 and time 2;

[0057] FIG7( a ) shows a phase unwrapping diagram for the phase filtering diagram at time 2 and time 1;

[0058] FIG7( b ) shows the phase unwrapping diagram for the phase filtering diagram at time 3 and time 2;

[0059] Figure 8(a) shows the time series wave height variation diagram of radar measurement for water surface status monitoring at time 2 and time 1;

[0060] Figure 8(b) shows the time series wave height variation diagram of radar measurement for water surface status monitoring at time 3 and time 2. DETAILED DESCRIPTION

[0061] The technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0062] Microwave remote sensing provides a method for hydrostatic state judgment and monitoring with its advantages of all-day, all-weather, unaffected by clouds and rain, and deformation measurement accuracy up to sub-millimeter level. Radar detects and measures the target in a non-contact manner by emitting electromagnetic waves and receiving the target's echo signal. By analyzing the time delay, frequency change, amplitude and other parameters of the signal, information such as the distance, speed, direction and shape of the target can be obtained. The active microwave remote sensing radar system can achieve real-time and dynamic monitoring of the area because of its sub-second sampling time. Combining the stepped frequency continuous wave (SFCW) technology with the timing interferometry measurement technology, the micro deformation is inverted according to the phase difference between the two measurement results, which has certain potential in the study of hydrostatic state judgment. The monitoring process is highly automated, which can achieve efficient real-time dynamic monitoring of hydrostatic state.

[0063] The present invention integrates static observation of radar system and time-series interference technology, which can realize the judgment of static water state and thus realize wide application in multiple fields. The radar system and method for judging static water state provided by the present invention can quickly and real-time obtain the water surface state through automated monitoring process, providing strong support for scientific research practice research.

[0064] The technical solution adopted by the present invention is to use radar differential interferometry technology, which combines stepped frequency continuous wave technology and differential interferometry technology. It is a radar active remote sensing measurement technology that repeatedly observes the same target area over a period of time to obtain time series radar images to extract deformation information.

[0065] The radar system used for judging the static water state includes: a coherent stepped-frequency continuous-wave radar subsystem and a data processing and control workstation.

[0066] The coherent stepped-frequency continuous-wave radar subsystem is used to transmit stepped-frequency continuous-wave signals to the water surface and receive echoes reflected from the water surface, and operates in the Ku band.

[0067] The data processing and control workstation uses interference technology to process the echo to obtain the phase change of the reflected signal in the radar irradiation area, and converts the measured phase change into deformation to monitor the changes in the water surface. Specifically, it includes: first extracting the phase of the echo signal at different time periods, interfering and filtering after removing the flat ground effect; then obtaining the phase true value of the time period through phase unwrapping, converting the phase change into deformation, and then obtaining the time series change information of the wave height during a period of time when the water surface tends to be calm; finally, determining the static water state of the water surface.

[0068] The present application utilizes a Ku-band stepped-frequency continuous wave radar installed on one side of the pool to form a coherent stepped-frequency continuous wave radar subsystem. The Ku-band radar consists of a coherent microwave transmitting and receiving device with a working band of 15 GHz center frequency and 6 GHz bandwidth, which can transmit and receive stepped-frequency continuous wave signals. The dual antennas of the Ku-band radar send and receive signals respectively. During operation, the positions of the transmitting and receiving antennas are fixed, and direct digital frequency synthesis (DDS) is used to generate a stepped-frequency continuous wave signal with a step of 7.5 MHz, a total of 800 frequency points, and a dwell time of 0.4 ms for each frequency point. A highly stable crystal oscillator is used to generate two reference signals, of which the 11.9 GHz signal is used for the mixer input of the transmitting link, and the 11.85 GHz signal is mixed with the stepped-frequency continuous wave signal, and is used as the local oscillator signal of the receiving link after passing through a bandpass filter circuit and an amplifier circuit. The stepped frequency continuous wave signal is up-converted, amplified, and band-pass filtered, and mixed with the 11.9GHz local oscillator signal to generate a radio frequency signal of the specified frequency band, which is transmitted to the transmitting antenna. When receiving the signal, the reference signal of the receiving antenna is down-converted, amplified, and band-pass filtered, and mixed with the 11.85GHz local oscillator signal to obtain a 50MHz single-frequency signal, which is sent to the A / D converter to complete digital sampling after passing through the band-pass filtering and amplification circuit. Figure 1 shown.

[0069] The Ku-band radar transmits and receives echo signals at the same time, statically observes the one-dimensional imaging of the water surface in the distance direction, and then interferes with the echo signal and post-processes it to obtain the wave height change, such as Figure 2 shown.

[0070] After determining the water surface that needs to be observed, place the radar and platform on the side of the pool based on the conditions on site that are suitable for setting up the radar. Set the radar's observation angle and scanning range based on the water surface height, and then observe the water surface.

[0071] The Ku-band static observation radar relies on a large bandwidth to obtain range resolution. It observes the echo energy changes in the beam-irradiated area in a real aperture manner, reflecting the changes in the backscatter coefficient caused by the change in wave height in the target area. The main parameters of the equipment are shown in Table 1.

[0072] Table 1 Ku-band altimeter radar related parameters

[0073]

[0074] The following introduces the principle of measuring the backscatter coefficient and wave height change of the water surface by Ku-band stepped frequency continuous wave static water observation radar. In order to obtain the echo information of different areas of the water surface, the radar system needs to have a certain distance resolution capability. Suppose the starting frequency of the radar system is f LWhen measuring the S parameters at this frequency, the transmit signal can be expressed by equation (1), where R represents the output impedance of the transmit port, and P t Indicates output power:

[0075]

[0076] Assume that the distances between the target point and the transmitting antenna and the receiving antenna are R t and R r , c represents the propagation speed of radar waves. According to the radar equation, the power measured at the receiving antenna can be expressed as formula (2), and then the receiving end signal can be expressed as formula (3):

[0077]

[0078] The S measured at this frequency 21 The parameters can be expressed as formula (4),

[0079]

[0080] A set of measured S 21 The parameter sequence can be expressed as formula (5),

[0081]

[0082] By performing IFFT on equation (5), range focusing can be completed and high resolution in the range direction can be obtained. The range resolution can be expressed by equation (7):

[0083]

[0084] In the azimuth direction, a certain distance resolution is obtained by real aperture method, and then the backscattering coefficient distribution of the target water area is calculated based on the calibration results of the corner reflector with known characteristics.

[0085] The Ku-band radar signal processing process has six steps, namely:

[0086] The first step is to obtain the time series signal

[0087] The phase of each observation pulse data is extracted in chronological order. When the water surface tends to be calm, continuous observation is performed to obtain the phases at three moments, as shown in Figure 3(a), Figure 3(b) and Figure 3(c).

[0088] Phase Interference

[0089] The phase interference diagram can be formed by conjugate multiplying the two radar images obtained at the previous and next moments, as shown in Figure 4(a) and Figure 4(b). Figure 4(a) is the interference phase diagram between moment 2 and moment 1, and Figure 4(b) is the interference phase diagram between moment 3 and moment 2.

[0090] Step 3: Remove the flat ground effect

[0091] The phase interference pattern obtained in the previous step is subjected to the ground flattening effect based on the frequency shift method. The frequency shift method has two working characteristics: one is that the fringes in the interference phase pattern change periodically, and the other is the frequency shift theorem in discrete Fourier transform.

[0092] Assuming that the power spectrum peak of point N appears at point l, the corresponding fringe frequency is:

[0093]

[0094] Among them, Δt represents the sampling time interval, R represents the distance from the antenna to the target, and the distance sampling rate is expressed as:

[0095]

[0096] In the frequency domain, the peak value is shifted to zero point, and the corresponding time domain phase correction value is:

[0097]

[0098] Where, the fringe frequency f fringe and the slant-range fringe frequency f fringe The relationship between ′ is:

[0099]

[0100] That is the following formula:

[0101]

[0102] For an M×N interference pattern, the specific steps to level the ground in the distance direction are as follows:

[0103] (1) Perform a discrete Fourier transform on each row. In order to eliminate the influence of noise on the interference fringe frequency and enhance the spectrum peak, the corresponding elements of the transformation results of each row are superimposed:

[0104]

[0105] Among them, x i (n) is an n-dimensional row vector, n is the number of pixels in the distance direction, and is used to store the n pixel values ​​of the i-th row of the interference pattern. The result of spectrum superposition is It is also an n-dimensional row vector and a k-dimensional unit row vector, that is, k=n. The digital frequency corresponding to its maximum value is the required peak frequency in the distance direction.

[0106] (2) For x i The Fourier transform of (n) is X i (k) Circular displacement with frequency l:

[0107] Y i (k) = X i (kl) N R N (k)(14)

[0108] in,() N Represents the remainder of N, R N (k) is a rectangular window of length N. This will move the peak frequency of the fringe to zero frequency. After circularly shifting each row of the interference pattern, the originally asymmetric range spectrum will become basically symmetrical.

[0109] Finally, for Y i (k) Perform inverse Fourier transform:

[0110] x′ i (n) = IDFT(Y i (k)) (15)

[0111] The result is equivalent to phase compensation of the distance pixels of the interference pattern in the spatial domain:

[0112] x′ i (n) = x i (n)W ln (16)

[0113] After performing the above operation on each row, the result of removing the flat ground in the range direction can be obtained. The flat ground effect removal method based on the frequency shift theory only uses the data of the interference pattern itself. It has a simple principle and a small amount of calculation. It is particularly suitable for flat areas such as water surfaces. The results are shown in Figure 5(a) and Figure 5(b). Figure 5(a) shows the results of removing the flat ground effect of the interference phase at time 2 and time 1, and Figure 5(b) shows the results of removing the flat ground effect of the interference phase at time 3 and time 2.

[0114] Step 4: Phase filtering

[0115] Since the random noise of the system and the noise caused by the image registration error will reduce the coherence of the interference pattern, the existence of residual points will lead to the inability to correctly perform phase unwrapping in the future, which will affect the accuracy of deformation inversion, so phase filtering of the interference pattern is also indispensable. An effective interference phase filtering method should filter out as much noise interference as possible and preserve the continuity of the fringes as well as possible. For random noise, the traditional mean filter, median filter, Gaussian filter, etc. can achieve ideal results.

[0116] Lee filter is a filter commonly used in SAR image denoising, which can enhance the local contrast of the image. It is a filter based on window function, and its principle is to perform weighted averaging of pixels within the window to eliminate noise in the image.

[0117] Assumptions represents the pixel value after filtering, x(p,q) represents the pixel value of the image before filtering, H represents the filter size, h(pi,qj) represents the distance to the center pixel (i,j), represents the variance of the noise, represents the sum of the squares of h. The formula for the Lee filter is:

[0118]

[0119] The specific Lee filter implementation process is as follows:

[0120] (1) Read the image to be filtered;

[0121] (2) Calculate the local mean and variance: For each pixel in the image, it is necessary to calculate the mean and variance of its local neighborhood. This is usually achieved through a sliding window. The window size can be determined based on the image size and noise intensity, usually between 3x3 and 11x11.

[0122] (3) Apply the filter. According to the mean and variance, Lee filter is applied to each pixel to achieve phase filtering. The results are shown in Figure 6(a) and Figure 6(b). Figure 6(a) is the phase filtering image after removing the flat ground effect at time 2 and time 1, and Figure 6(b) is the phase filtering image after removing the flat ground effect at time 3 and time 2.

[0123] Step 5: Phase Unwrapping

[0124] The obtained phase is unwrapped to obtain the accurate phase information corresponding to the deformation information. When the magnitude of the micro-deformation is greater than λ / 2, the real interference phase φ will be entangled in the main value Since the phase value of the interference pattern is modulo 2π, and the main value range is between -π and π, 2kπ (k is an arbitrary integer) needs to be added to obtain the phase that reflects the actual deformation variable, and then the deformation variable of the target is inverted:

[0125]

[0126] The least squares method is a global phase unwrapping algorithm. The advantages of this algorithm are that the solution is unique and optimal in a certain sense; fast algorithms such as discrete cosine transform or FFT can be used to further improve the computational efficiency; there is no need to specially process residual points, so the errors caused by residual points will not propagate globally; but the disadvantage is that when the data quality is low and the residual points are dense, the accuracy of unwrapping becomes poor. The basic idea of ​​the least squares method is to wrap the phase The first-order differences of the transverse and longitudinal directions should be equal to the first-order differences of the unwrapping phase φ(m,n). In order to minimize the difference between the two, the mean square error between the two is taken as the objective function. By minimizing the objective function, the unwrapping phase that is closest to the entanglement phase in the least squares sense can be obtained.

[0127] Assume that the unwrapped phase matrix is The size of the interference phase diagram, i.e. the winding phase matrix φ(m,n), is M×N, and the difference between the row vector x and the column vector y is denoted by Δ x (m,n) and Δ y (m,n):

[0128]

[0129] The solution of the minimum 2-norm method satisfies:

[0130]

[0131] The optimal solution can be obtained by taking the partial derivative of φ(m,n) and setting the partial derivative to 0. Therefore, the solution of the least squares method is:

[0132]

[0133] The above formula satisfies the discrete form of Poisson's equation, namely:

[0134]

[0135] Usually, given a Newman boundary condition, a set of definite solutions to the Poisson equation can be obtained. However, when φ(m,n) is a periodic function, the Newman boundary is not needed. Therefore, it is possible to consider extending the interference phase image into a periodic image and then using discrete cosine transform or FFT to solve it. Performing a two-dimensional mirror reflection on the image and then periodically extending it can ensure the smoothness of the extended boundary. The specific implementation process of the least squares method using FFT is as follows:

[0136] (1) First, calculate the two-dimensional phase gradient of the interference phase image, Δ x (m,n) and Δ y (m,n), denoted as ρ;

[0137] (2) Then the phase gradient image is mirrored along the upper and right boundary symmetry axes of the image to obtain an area four times the size of the original interference phase image, denoted as

[0138] (3) Yes Perform FFT transformation, recorded as P(m,n);

[0139] (4) Combining the mirror transformation process with the definition and properties of the two-dimensional discrete Fourier transform, we can obtain:

[0140]

[0141] Where M and N represent the number of rows and columns in the interference phase image, respectively. Therefore, the value ranges of m and n are [0, M] and [0, N], respectively.

[0142] But when m=n=0, this formula is meaningless, so we can directly define

[0143] (5) Perform IFFT transformation on φ(m,n), which is expressed as Phase unwrapping can be achieved by taking the first (M+1) rows and the first (N+1) columns. The results are shown in Figure 7(a) and Figure 7(b). Figure 7(a) shows the phase unwrapping result after phase filtering at time 2 and time 1, and Figure 7(b) shows the phase unwrapping result after phase filtering at time 3 and time 2.

[0144] Step 6: Inversion of wave height changes

[0145] Because the radar system is a static observation, the platform parameters are fixed at this time, and the interference phase and the target height can be considered to be approximately linear. The change trend of the interference phase can clearly represent the change trend of the water surface wave height. By processing a series of complex images in the time series, the time series change of the water surface wave height can be dynamically monitored, and the static water state can be judged.

[0146] After processing, the phase containing deformation information can be extracted. c It represents the center frequency of the radar system's acquisition signal. The change in the target's position can be obtained based on the phase difference between two target echoes:

[0147]

[0148] The initial phase difference is taken upward at the image sampling time, and the temporal micro-deformation of the surface wave height in the image is obtained by multiple accumulation. The results are shown in Figures 8(a) and 8(b). Figure 8(a) is a diagram showing the change of wave height between time 2 and time 1, and Figure 8(b) is a diagram showing the change of wave height between time 3 and time 2. It is easy to see that the range of wave height change between time 3 and time 2 is smaller than that between time 2 and time 1, indicating that the water surface tends to be in a static water state at this time, which is consistent with the actual measurement situation. This verifies the effectiveness of the Ku-band stepped-frequency continuous wave static water observation radar system and measurement method for static water state monitoring and judgment.

[0149] Ideally, the change in wave height of the water surface in the static water state is 0. In actual situations, the static water state is affected by many factors and the measurement error of the system itself, so the wave height of the water surface is not 0. Therefore, a certain threshold is selected. When the change in the wave height of the water surface during a certain period of time is less than the threshold, it can be determined as a static water state. The experiment takes three moments as an example. In addition, continuous observation and collection and processing of data at multiple moments can be carried out to monitor the state of the water surface in real time and monitor the whole process of the water surface returning to the static water state. Then, the time required to restore the static water state in a specific experimental scenario can be determined to provide strong support for scientific research and practice.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application is described in detail with reference to the embodiments, a person skilled in the art should understand that any modification or equivalent replacement of the technical solution of the present application does not depart from the spirit and scope of the technical solution of the present application and should be included in the scope of the claims of the present application.

Claims

1. A radar system for judging still water conditions, characterized in that: The system comprises: A coherent stepped-frequency continuous-wave radar subsystem for transmitting stepped-frequency continuous-wave signals to the water surface and receiving echoes reflected from the water surface, operating in the Ku band; and The data processing and control workstation is used to process the echoes using interference technology to obtain the phase change of the reflected signal in the radar illumination area, and convert the measured phase change into deformation to realize the monitoring of water surface changes; The working process of the data processing and control workstation includes: firstly extracting the phase of the echo signal at different time periods, interfering and filtering after removing the flat ground effect; then obtaining the phase true value of the time period through phase unwrapping, converting the phase change into deformation, and then obtaining the time series change information of the wave height over a period of time when the water surface is becoming calm; finally, judging the still water state of the water surface according to the wave height of the water surface.

2. The radar system for judging still water conditions according to claim 1, characterized in that: The working process of the data processing and control workstation includes: Step 1: Extract the phase of each observation pulse data in chronological order. When the water surface tends to be calm, observe continuously and obtain the phases at three moments. Step 2: Conjugate multiply the two radar images acquired at the previous and next moments to form a phase interference diagram; Step 3: Remove the flat ground effect from the phase interferogram based on the frequency shift method; Step 4: Perform phase filtering on the phase interference pattern after removing the flat ground effect; Step 5: Perform phase unwrapping processing on the phase interference image after phase filtering to obtain accurate phase information corresponding to the deformation information; Step 6: The change in water surface wave height is obtained based on the phase difference between the two target echoes. When the wave height change is less than the set threshold, it is determined to be a still water state.

3. The radar system for judging still water conditions according to claim 2, characterized in that: The step 3 comprises: Step 3-1: Perform discrete Fourier transform on each row of the phase interference pattern, and superimpose the corresponding elements of the transformation results of each row to obtain the result of spectrum superposition. Where M represents the number of rows of the phase interferogram; FFT represents discrete Fourier transform; x i (n) is an n-dimensional row vector, which is used to store the n pixel values ​​of the i-th row of the phase interference pattern. n is the number of pixels in the range direction. The result of spectrum superposition is It is also an n-dimensional vector, and a k-dimensional unit row vector, that is, k = n, and the digital frequency l corresponding to its maximum value is the required peak frequency in the distance direction; Step 3-2: For x i The Fourier transform of (n) is X i (k) Circular displacement with frequency l: Y i (k)=X i (k)-l) N R N (k) in,() N Represents the remainder of N, R N (k) is a rectangular window of length N; N represents the number of columns of the phase interference pattern; Step 3-3: For Y i (k) Perform inverse Fourier transform.

4. The radar system for judging still water conditions according to claim 2, characterized in that: The step 4 comprises: Step 4-1: Read the image to be filtered; Step 4-2: For each pixel in the image, calculate the mean and variance of its local neighborhood through a sliding window; Step 4-3: Apply Lee filter to each pixel based on the mean and variance to achieve phase filtering.

5. The radar system for judging still water conditions according to claim 2, characterized in that: The step 5 comprises: Step 5-1: Calculate the two-dimensional phase gradient of the phase interference pattern respectively, denoted as ρ; Step 5-2: Mirror the two-dimensional phase gradient with the upper and right boundaries of the image as the symmetry axis to obtain an area four times the size of the original phase interference pattern, recorded as Step 5-3: Perform discrete Fourier transform, denoted as P(m,n); the value ranges of m and n are [0,M] and [0,N] respectively, and M and N represent the number of rows and columns in the interference phase image respectively; Step 5-4: According to the mirror transformation process and the properties of the two-bit discrete Fourier transform, the unwrapped phase φ(m,n) is obtained: Among them, setting Step 5-5: Perform inverse Fourier transform on φ(m,n) and take the first (M+1) rows and first (N+1) columns to achieve phase unwrapping.

6. The radar system for judging still water conditions according to claim 2, characterized in that: The step 6 comprises: Where ΔR represents the change in water surface wave height; Δφ represents the phase difference between two target echoes; c represents the propagation speed of radar waves; f c Indicates the center frequency of the signal collected by the radar system.

7. A method for judging static water state, implemented based on the system according to any one of claims 1 to 6, the method comprising: The coherent stepped frequency continuous wave radar subsystem transmits a stepped frequency continuous wave signal to the water surface and receives the echo reflected from the water surface; The data processing and control workstation uses interference technology to process the echo to obtain the phase change of the reflected signal in the radar illumination area, and converts the measured phase change into deformation to monitor the changes in the water surface.

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