A radar system and method for judging hydrostatic state

Abstract

The present application provides a radar system and method for judging the state of still water, the system comprising: 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 a data processing and control workstation for processing the echoes using interference technology to obtain the phase change of the reflected signal in the radar irradiation area, converting the measured phase change into deformation to monitor the changes in the water surface. The advantages of the present application are: high measurement accuracy at the submillimeter level, a non-contact measurement method that can effectively ensure that personnel can obtain water surface change information within a safe range; flexible installation, short observation period, and low cost, which effectively improves the efficiency of water surface change monitoring and subsequent project implementation.

Description

Technical Field

[0001] This application belongs to the technical field of still water state judgment, and particularly relates to a radar system and method for still water state judgment. Background Art

[0002] The judgment of the still water state usually refers to the state where the water flow is stable, there are no hydrological changes such as sudden rises and falls, the flow velocity at a certain point in the water body is zero, and there is no obvious vertical flow. It can be judged by means of naked-eye observation, stagnation point measurement with a current meter, sonar measurement, radar measurement, etc. The judgment of the still water state has wide applications in multiple fields. In water conservancy projects, the judgment of the still water state plays an important role in the design of dams, reservoirs, and other water conservancy facilities, and can be used to analyze water pressure, calculate hydrostatic pressure, and ensure the safety and stability of the structure; in environmental monitoring, the still water state of the water body helps to evaluate the ecological health of the water body and judge whether there are pollution sources; in shipping and port management, the still water state affects the docking and navigation safety of ships and is used to optimize the layout of berths and waterways; the still water state also has important applications in the design of irrigation systems, which can help design a reasonable water flow distribution to ensure that crops obtain sufficient water sources. The judgment of the still water state not only helps experts in scientific research and engineering design, but also has a certain impact on the lives and safety of the public.

[0003] Subtle changes in the water surface cannot be judged by the naked eye, and stagnation point measurement with a current meter requires contact with the water surface, which poses a certain challenge for areas that are difficult to reach. Sonar has certain advantages in measuring water surface changes. Among them, ultrasonic sensors can be installed on the water edge without the equipment directly entering the water. The synthetic aperture sonar (SAS) technology can perform fine imaging of the water surface, and monitor the changes in the water surface state through image processing technology, with high precision, real-time performance, and remote detection capabilities. However, the processing and analysis of sonar data may be relatively complex, requiring professional knowledge and software support, and also facing challenges such as environmental dependence and equipment costs. Summary of the Invention

[0004] The purpose of this application is to overcome the defects of difficult installation and the need for contact measurement in the prior art.

[0005] To achieve the above purpose, this application proposes a radar system for still water state judgment, and the system includes:

[0006] A coherent stepped-frequency continuous wave radar subsystem, which is used to transmit a stepped-frequency continuous wave signal to the water surface and receive the echo reflected from the water surface, and operates in the Ku band; and

[0007] A data processing and control workstation, which is used to process the echo 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 the water surface change;

[0008] The working process of the data processing and control workstation includes: First, extract the phases of the echo signals in different time segments, perform interference, then filter after removing the flat-earth effect; Then, obtain the true phase values of the time segments through phase unwrapping, convert the phase changes into deformations, and further obtain the time-series change information of the wave height of the water surface for a period of time during the process of the water surface tending to be calm; Finally, determine 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 phases of the data of each observation pulse in chronological order. When the water surface tends to be calm, continuously observe and obtain the phases at three moments;

[0011] Step 2: Conjugate multiply the two radar images obtained at the previous and subsequent moments to form a phase interference pattern;

[0012] Step 3: Remove the flat-earth effect on the phase interference pattern based on the frequency shift method;

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

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

[0015] Step 6: Obtain the change in the wave height of the water surface according to the phase difference between the two target echoes. When the change in the wave height is less than the set threshold, it is determined as the still water state.

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

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

[0018]

[0019] where M represents the number of rows of the phase interference pattern; FFT represents the discrete Fourier transform; x i (n) is an n-dimensional row vector 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. Then the result of spectrum superposition is also an n-dimensional row vector and is 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 range direction;

[0020] Step 3-2: Perform a Fourier transform on x i (n) to obtain Xi (k) Perform circular shift at a frequency:

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

[0022] where () N denotes taking the remainder with respect to N, and 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: Perform the inverse Fourier transform on Y i (k).

[0024] As an improvement to the above system, the said 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 the Lee filter to each pixel according to the mean and variance to achieve phase filtering.

[0028] As an improvement to the above system, the said 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: Perform a mirror transformation on the two - dimensional phase gradient with the upper boundary and the right boundary of the image as the symmetry axes to obtain a region four times the size of the original phase interference pattern, denoted as

[0031] Step 5 - 3: Perform a discrete Fourier transform on , 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 pattern respectively;

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

[0033]

[0034] where, it is set that

[0035] Step 5 - 5: Perform the inverse Fourier transform on φ(m, n), and take its first (M + 1) rows and 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 the water surface wave height; △φ represents the phase difference between two target echoes; c represents the radar wave propagation speed; f c represents the center frequency of the signal collected by the radar system.

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

[0040] The coherent stepped-frequency continuous wave radar subsystem emits 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 the 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 realize the monitoring of the water surface change.

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

[0043] 1. The present invention proposes a radar system and method for judging the still water state. Technical solutions are selected according to the characteristics of the still water state, combining the SFCW system with the interference technology. The SFCW radar has the advantages of small volume, 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, providing a non-contact measurement method with flexible installation, short observation period, and low cost for the judgment and monitoring of the still water state, and effectively improving the efficiency of water surface change monitoring and subsequent project implementation;

[0044] 2. The present invention adopts the Ku-band radar static observation technology for the water surface scattering characteristics. The antenna adopts the method of separating the transmitting and receiving antennas, emits the SFCW signal, and continuously collects the radar echo signal to realize the real-time monitoring of the water surface change;

[0045] 3. The method and system proposed by the present invention can adjust the radar's viewing angle, scanning length, and signal bandwidth according to the distance and viewing angle between the observation position and the water surface, which is convenient for erection. The present invention supports synchronous observation with optical devices, and it is convenient for monitoring the water surface change in the area after multi-source data fusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The figure shows the working principle block diagram of the radar system for judging and monitoring the still water state;

[0047] Figure 2The following is a block diagram of the overall process structure of the radar system;

[0048] Figure 3(a) shows the phase diagram obtained at the first moment of continuous radar measurement for water surface state monitoring;

[0049] Figure 3(b) shows the phase diagram obtained at the second moment of continuous radar measurement for water surface state monitoring;

[0050] Figure 3(c) shows the phase diagram obtained at the third moment of continuous radar measurement for water surface state monitoring;

[0051] Figure 4(a) shows the phase diagram of the interference between the second moment and the first moment;

[0052] Figure 4(b) shows the phase diagram of the interference between the third moment and the second moment;

[0053] Figure 5(a) shows the flat-earth effect removal diagram for the phase interference diagram between the second moment and the first moment;

[0054] Figure 5(b) shows the flat-earth effect removal diagram for the phase interference diagram between the third moment and the second moment;

[0055] Figure 6(a) shows the phase filtering diagram for the flat-earth effect removal diagram between the second moment and the first moment;

[0056] Figure 6(b) shows the phase filtering diagram for the flat-earth effect removal diagram between the third moment and the second moment;

[0057] Figure 7(a) shows the phase unwrapping diagram for the phase filtering diagram between the second moment and the first moment;

[0058] Figure 7(b) shows the phase unwrapping diagram for the phase filtering diagram between the third moment and the second moment;

[0059] Figure 8(a) shows the radar measurement time series wave height change diagram for water surface state monitoring between the second moment and the first moment;

[0060] Figure 8(b) shows the radar measurement time series wave height change diagram for water surface state monitoring between the third moment and the second moment. Specific implementation manners

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

[0062] Microwave remote sensing, with its advantages of all-weather and all-day operation, being unaffected by clouds and rain, and having a deformation measurement accuracy up to the sub-millimeter level, provides a method for the judgment and monitoring of the still water state. The radar uses a non-contact measurement method to detect and range the target by transmitting electromagnetic waves and receiving the echo signals of the target. Information such as the distance, speed, direction, and shape of the target is obtained by analyzing parameters such as the time delay, frequency change, and amplitude of the signal. The active microwave remote sensing radar system can achieve real-time and dynamic monitoring of the area because its sampling time reaches the sub-second level. By combining the stepped-frequency continuous wave (SFCW) technology and the time series interferometry technology, the micro-deformation is inverted according to the phase difference between the results of two consecutive measurements, which has certain potential in the research of the still water state judgment. The monitoring process is highly automated, enabling efficient real-time dynamic monitoring of the still water state.

[0063] The present invention combines the static observation of the radar system and the time series interferometry technology to achieve the judgment of the still water state, and then realizes wide applications in multiple fields. The radar system and method for the still water state judgment provided by the present invention have an automated monitoring process that can quickly and real-time obtain the water surface state, providing strong support for scientific research and practical research.

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

[0065] The radar system for the still water state judgment 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 the echoes reflected by the water surface, and operates in the Ku band.

[0067] The data processing and control workstation uses the interferometry technology to process the echoes to obtain the phase change of the reflected signals in the radar illumination area, and converts the measured phase change into deformation to achieve the monitoring of the water surface change. Specifically, it includes: first, extracting the phases of the echo signals in different time periods, filtering after removing the flat-earth effect through interference; then obtaining the true phase values of the time periods through phase unwrapping, converting the phase change into deformation, and further obtaining the time series change information of the wave height of the water surface within a period of time during the process of tending to be calm; finally, determining the still water state of the water surface.

[0068] This application uses 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 centered at a frequency of 15 GHz and a bandwidth of 6 GHz, which can transmit and receive stepped-frequency continuous-wave signals. The Ku-band radar's dual antennas transmit and receive signals respectively. During operation, the positions of the transmitting and receiving antennas remain fixed. 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 high-stability crystal oscillator is used to generate two reference signals. Among them, the 11.9 GHz signal is used as the mixer input for the transmitting link, and the 11.85 GHz signal is mixed with the stepped-frequency continuous-wave signal. After passing through a band-pass filter circuit and an amplifier circuit, it serves as the local oscillator signal for the receiving link. The stepped-frequency continuous-wave signal undergoes up-conversion, amplification, and band-pass filtering, and is mixed with the 11.9 GHz local oscillator signal to generate a radio frequency signal in the specified frequency band, which is transmitted to the transmitting antenna. When receiving signals, the reference signal of the receiving antenna undergoes down-conversion, amplification, and band-pass filtering, and is mixed with the 11.85 GHz local oscillator signal to obtain a single-frequency signal of 50 MHz. After passing through a band-pass filter and an amplifier circuit, it is sent to an A / D converter to complete digital sampling, as Figure 1 shown.

[0069] The Ku-band radar simultaneously transmits and receives echo signals, statically observes the one-dimensional imaging of the water surface in the range direction, and then takes the phase of the echo signal for interference and post-processing to obtain the wave height change, as Figure 2 shown.

[0070] After determining the water surface to be observed, according to the situation of the suitable radar installation position on-site, place the radar and the platform on the side of the pool. After setting the observation angle and scanning range of the radar according to the water surface height, observe the water surface.

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

[0072] Table 1 Related Parameters of Ku-band Altimetry Radar

[0073]

[0074] The following introduces the principle of the Ku-band stepped-frequency continuous-wave still water observation radar for measuring the backscattering coefficient of the water surface and the wave height change. In order to obtain the echo information of different areas of the water surface, the radar system needs to have a certain range resolution. Let the starting frequency of this radar system be f L, when measuring the S-parameters of this frequency point, the transmitted signal can be expressed by Equation (1), where R represents the output impedance of the transmitting port, and P t represents the 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 the radar wave. According to the radar equation, the power measured at the receiving antenna can be expressed by Equation (2), and then the received signal can be expressed by Equation (3):

[0077]

[0078] The S 21 parameters measured at this frequency point can be expressed by Equation (4),

[0079]

[0080] A set of measured S 21 parameter sequences can be expressed by Equation (5),

[0081]

[0082] Performing IFFT on Equation (5) can complete the range focusing and obtain high range resolution. The range resolution can be expressed by Equation (7):

[0083]

[0084] In the azimuth direction, a certain range resolution is obtained in the real aperture mode. Then, based on the calibration results of the known characteristic corner reflector, the backscattering coefficient distribution of the target water area is calculated.

[0085] The process of Ku-band radar signal processing has six steps, which are respectively:

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

[0087] Extract the phase of the data of each observation pulse in chronological order. When the water surface tends to be calm, continuously observe and obtain the phases at three moments, as shown in Figures 3(a), 3(b) and 3(c).

[0088] The second step is phase interference

[0089] Conjugate multiply the two radar images obtained at the previous and subsequent moments to form a phase interference pattern, as shown in Figures 4(a) and 4(b). Figure 4(a) is the interference phase pattern between moment 2 and moment 1, and Figure 4(b) is the interference phase pattern between moment 3 and moment 2.

[0090] The third step: removing the flat earth effect

[0091] Based on the frequency shift method, the flat earth effect is removed from the phase interference pattern obtained in the previous step. The frequency shift method has two working characteristics: first, the fringes in the interference phase pattern change periodically; second, the frequency shift theorem in the discrete Fourier transform.

[0092] Assume that the peak of the power spectrum at N points appears at point l. Then the corresponding fringe frequency is:

[0093]

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

[0095]

[0096] In the frequency domain, the peak is circularly shifted to zero. The corresponding time-domain phase correction value is:

[0097]

[0098] where the fringe frequency f fringe and the slant range fringe frequency f fringe ′ have the following relationship:

[0099]

[0100] That is, the following formula:

[0101]

[0102] For an M×N interference pattern, the specific steps for removing the flat earth effect in the range direction are as follows:

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

[0104]

[0105] where x i (n) is an n-dimensional row vector, where n is the number of pixels in the range direction and is used to store the n pixel values of the i-th row of the interference pattern. Then the result of the spectral superposition is also an n-dimensional row vector and is 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 range direction.

[0106] (2) Circularly shift the Fourier transform X i (n) of x i (k) by the frequency l:

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

[0108] Among them, () N represents taking the remainder with respect to N, and R N (k) is a rectangular window of length N. In this way, the peak frequency of the fringe is shifted to the zero frequency. After circularly shifting each row of the interferogram, the originally asymmetric azimuth spectrum will become basically symmetric.

[0109] Finally, perform the inverse Fourier transform on Y i (k):

[0110] x′ i (n) = IDFT(Y i (k)) (15)

[0111] The result is equivalent to performing phase compensation on the azimuth pixels of the interferogram in the spatial domain:

[0112] x′ i (n) = x i (n)W ln (16)

[0113] After performing the above operations on each row, the azimuth de - flattening result can be obtained. The method for removing the flat - earth effect based on the frequency - shift theory only uses the data of the interferogram itself, has a simple principle and a small amount of calculation, and is especially suitable for flat areas such as the water surface. The results are shown in Figures 5(a) and 5(b). Figure 5(a) is the result of removing the flat - earth effect of the interferometric phase at time 2 and time 1, and Figure 5(b) is the result of removing the flat - earth effect of the interferometric phase at time 3 and time 2.

[0114] Fourth step: Phase filtering

[0115] Since the noise caused by the random noise of the system and the image registration error will reduce the coherence of the interferogram, and the existence of residual points will lead to incorrect subsequent phase unwrapping and thus affect the accuracy of deformation inversion, so the phase filtering of the interferogram is also essential. An effective interferometric 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, using traditional mean filtering, median filtering, Gaussian filtering, etc. can obtain ideal results.

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

[0117] Assume represents the pixel value after filtering, \(x(p,q)\) represents the pixel value of the image before filtering, \(H\) represents the filter size, and \(h(p - i,q - j)\) represents the distance to the central 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 implementation process of the Lee filter 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, its local neighborhood mean and variance need to be calculated. Usually, it is achieved by sliding a window, and the window size can be determined according to the image size and noise intensity, usually between 3x3 and 11x11;

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

[0123] The fifth step: Phase unwrapping

[0124] Perform phase unwrapping on the obtained phase to obtain the accurate phase information corresponding to the deformation information. When the magnitude of the micro-deformation is greater than \(\lambda / 2\), the true interference phase \(\varphi\) will be wrapped within the principal value range. Since the phase value of the interferogram is modulo \(2\pi\) and the principal value range is between \(-\pi\) and \(\pi\), \(2k\pi\) (\(k\) is an arbitrary integer) needs to be added to obtain the phase reflecting the true deformation amount, and then the deformation amount of the target can be inversely obtained:

[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 calculation efficiency; there is no need to specifically handle the residual points, so the errors caused by the residual points will not spread globally; however, the disadvantage is that when the data quality is low and the residual points are dense, the accuracy of the unwrapping becomes poor. The basic idea of the least squares method is that the wrapped phase The first-order differences in the horizontal and vertical directions should be equal to the first-order differences of the unwrapped phase φ(m,n). To minimize the difference between the two, the mean square error of the two is used as the objective function, and minimizing this objective function can obtain the unwrapped phase that is closest to the wrapped phase in the least squares sense.

[0127] Assume that the phase matrix after unwrapping is The interference phase diagram, that is, the size of the wrapped phase matrix φ(m,n) is M×N. The difference values in the row vector x and column vector y directions are denoted as Δ x (m,n) and Δ y (m,n):

[0128]

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

[0130]

[0131] Taking the partial derivative of φ(m,n) and setting the partial derivative to 0 can obtain the optimal solution. Therefore, the solution of the least squares method is:

[0132]

[0133] The above equation satisfies the discrete form of the Poisson equation, that is:

[0134]

[0135] Usually, when a Neumann boundary condition is given, a set of definite solutions of the Poisson equation can be obtained. However, when φ(m,n) is a periodic function, the Neumann boundary is not required. Therefore, the interference phase diagram can be extended to a periodic image and then solved using the discrete cosine transform or FFT. Mirror reflection of the image in two dimensions followed by periodic extension 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 gradients of the interference phase diagram, Δ x (m,n) and Δ y (m,n), denoted as ρ;

[0137] (2) Then, perform mirror transformation on the phase gradient diagram with the upper boundary and right boundary symmetry axes of the image to obtain a region four times the size of the original interference phase diagram, denoted as

[0138] (3) Perform FFT transformation on and denote it as P(m,n);

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

[0140]

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

[0142] However, when m = n = 0, this formula is meaningless, so it is directly defined as

[0143] (5) Perform an IFFT transformation on φ(m, n), denoted as Taking the first (M + 1) rows and the first (N + 1) columns can achieve phase unwrapping. The results are shown in Figures 7(a) and 7(b). Figure 7(a) is the phase unwrapping result after phase filtering at time 2 and time 1, and Figure 7(b) is the phase unwrapping result after phase filtering at time 3 and time 2.

[0144] Sixth step: Inversion of wave height change

[0145] Since this radar system is for static observation and the platform parameters are fixed at this time, it can be considered that there is an approximately linear relationship between the interference phase and the target height. Furthermore, the change trend of the water surface wave height can be clearly characterized from the change trend of the interference phase. By processing a series of complex images in the time series, the dynamic monitoring of the temporal change of the water surface wave height can be realized, and then the judgment of the still water state can be achieved.

[0146] After processing, the phase containing deformation information can be extracted. f c represents the center frequency of the signal collected by the radar system. According to the phase difference between the two target echoes, the position change of the target can be obtained:

[0147]

[0148] Differencing with the initial phase respectively upward at the image sampling time, and accumulating multiple times can obtain the temporal micro-deformation of the surface wave height in the image. The results are shown in Figures 8(a) and 8(b). Figure 8(a) is the change diagram of the wave height between time 2 and time 1, and Figure 8(b) is the change diagram of the wave height between time 3 and time 2. It is easy to see that the wave height change range between time 3 and time 2 is smaller than the wave height change range between time 2 and time 1, indicating that the water surface tends to be in a still water state at this time, which is consistent with the actual measurement situation. Thus, the effectiveness of this Ku-band stepped-frequency continuous-wave still water observation radar system and measurement method for monitoring and judging the still water state is verified.

[0149] Ideally, the wave height change of the water meter in the still water state is 0. In actual situations, due to various factors and measurement errors of the system itself, the wave height of the water meter in the still water state is not 0. Therefore, a certain threshold is selected. When the wave height change of the water surface within a certain period of measurement is less than this threshold, it can be determined as the still water state. Taking 3 moments as an example in the experiment, in addition, continuous observation and data acquisition can be carried out at multiple moments to monitor the water surface state in real time and the whole process of the water surface recovering to the still water state, and then the time required to recover to the still water state in a specific experimental scenario can be judged, providing strong support for scientific research practice.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the present application has been described in detail with reference to the embodiments, those of ordinary skill in the art should understand that any modification or equivalent replacement of the technical solutions of the present application does not depart from the spirit and scope of the technical solutions of the present application, and they should all be covered by the scope of the claims of the present application.

Claims

1. A radar system for judging the still water state, characterized in that, The system includes: A coherent stepped-frequency continuous-wave radar subsystem for transmitting a stepped-frequency continuous-wave signal to the water surface and receiving the echo reflected from the water surface, operating in the Ku band; and A data processing and control workstation for processing the echo using interference technology to obtain the phase change of the reflected signal in the radar illumination area, converting the measured phase change into deformation, and realizing the monitoring of the water surface change; The working process of the data processing and control workstation includes: first, extracting the phases of the echo signals in different time periods, filtering after interference and removing the flat-earth effect; then obtaining the true phase value of the time period through phase unwrapping, converting the phase change into deformation, and further obtaining the time-series change information of the wave height of the water surface for a period of time during the process of the water surface tending to be 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 static water state judgment according to claim 1, wherein The working process of the data processing and control workstation includes: Step 1: Extract the phase of the data of each observation pulse in chronological order. When the water surface tends to be calm, continuously observe and obtain the phases at three moments; Step 2: Conjugately multiply the two radar images obtained at the previous and subsequent moments to form a phase interference pattern; Step 3: Remove the flat-earth effect on the phase interference pattern based on the frequency shift method; Step 4: Perform phase filtering on the phase interference pattern after removing the flat-earth effect; Step 5: Perform phase unwrapping processing on the phase interference pattern after phase filtering to obtain the accurate phase information corresponding to the deformation information; Step 6: Obtain the change in the wave height of the water surface according to the phase difference between the two target echoes. When the change in the wave height is less than the set threshold, it is judged as the still water state.

3. The radar system for judging the still water state according to claim 2, wherein, The said Step 3 includes: 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 interference pattern; FFT represents the discrete Fourier transform; x i (n) is an n-dimensional row vector used to store the n pixel values of the i-th row of the phase interference pattern, and n is the number of pixels in the range direction. Then the result of spectrum superposition is also an n-dimensional vector and is a k-dimensional unit row vector, that is, k = n. The digital frequency l corresponding to its maximum value is the required peak frequency in the range direction; Step 3-2: For x i (n), perform a circular shift of its Fourier transform X i (k) at frequency l: Y i (k) = X i (k) - l) N R N (k) Among them, () N represents the remainder of division by 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: Perform the inverse Fourier transform on Y i (k).

4. The radar system for static water state determination according to claim 2, characterized in that, The said Step 4 includes: 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 the Lee filter to each pixel according to the mean and variance to achieve phase filtering.

5. The radar system for judging the still water state according to claim 2, characterized in that, The said Step 5 includes: Step 5-1: Calculate the two-dimensional phase gradient of the phase interference pattern respectively, denoted as ρ; Step 5-2: Perform a mirror transformation on the two-dimensional phase gradient with the upper and right boundaries of the image as the symmetry axes to obtain a region four times the size of the original phase interference pattern, denoted as Step 5-3: Perform discrete Fourier transform on and denote it as P(m,n); the value ranges of m and n are [0,M] and [0,N] respectively, where M and N represent the number of rows and columns in the interference phase diagram; Step 5-4: Obtain the unwrapped phase φ(m,n) according to the mirror transformation process and the properties of the two-dimensional discrete Fourier transform: Among them, set Step 5-5: Perform the inverse Fourier transform on φ(m,n), and take the first (M + 1) rows and the first (N + 1) columns to achieve phase unwrapping.

6. The radar system for static water state judgment according to claim 2, wherein The said Step 6 includes: where ΔR represents the change in water surface wave height; Δφ represents the phase difference between two target echoes; c represents the radar wave propagation speed; f c represents the center frequency of the signal collected by the radar system.

7. A method for judging the still water state, implemented based on the system according to any one of claims 1-6, the method includes: 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, converts the measured phase change into deformation, and realizes the monitoring of the water surface change.

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