A method and system for multi-channel data processing of river flow measurement side-scan radar

Abstract

This invention relates to the field of hydrological monitoring technology, specifically disclosing a multi-channel data processing method and system for river flow measurement side-scan radar. The invention involves performing a first FFT transform on the radar echo signal to obtain the range; performing a second FFT transform on the radar echo signal to obtain the velocity; acquiring the Doppler power spectrum; extracting effective frequency points from the Doppler power spectrum; and performing multi-channel data processing fuzzy mathematical inversion to estimate the angles of the effective frequency points, thus obtaining the azimuth angle. By obtaining range and velocity information through two FFT transforms and combining the multi-channel data processing fuzzy mathematical inversion method to estimate the angles of each effective frequency point, a flow field with a one-to-one correspondence between range, velocity, and azimuth angle can be obtained, ensuring that each radial velocity in the flow field simultaneously possesses range, velocity, and azimuth angle information.

Description

Technical Field

[0001] This invention belongs to the field of hydrological monitoring technology, and in particular relates to a method and system for processing multi-channel data from a river flow measurement side-scan radar. Background Technology

[0002] Water surface waves are undulating, complex, and highly variable, with almost no discernible pattern. Therefore, the study and monitoring of water flow is extremely important for most countries. French mathematician Fourier proposed a theory: any periodic function can be represented by an infinite series of sine and cosine functions. Based on Fourier's theory, complex water surface states can be represented by the superposition of multiple sine waves with different amplitudes, phases, carrier frequencies, and propagation directions.

[0003] In hydrological monitoring technology, the flow field detected by ultra-high frequency radar is a fan-shaped area. Each range cell is divided into multiple surface targets according to the range resolution and angular resolution. Although the velocity information of multiple surface targets at each range cell is obtained after processing, it is impossible to correspond each velocity to each surface target at that range cell. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-channel data processing method and system for river flow measurement side-scan radar, aiming to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] A method for processing multi-channel data from a river flow measurement side-scan radar, the method specifically including the following steps:

[0007] Perform a first FFT transform on the radar echo signal to determine the range;

[0008] The radar echo signal is subjected to a second FFT transform to determine the velocity.

[0009] Obtain the Doppler power spectrum and extract the effective frequency points from the Doppler power spectrum;

[0010] The effective frequency points are processed using multi-channel data fuzzy mathematical inversion to estimate the angle, thus obtaining the azimuth angle.

[0011] As a further limitation of the technical solution of this embodiment of the invention, the method further includes the following steps:

[0012] Before performing multi-channel data processing and fuzzy mathematical inversion angle estimation on the effective frequency points, active or passive calibration is performed.

[0013] As a further limitation of the technical solution of this embodiment of the invention, the active calibration is: acquiring a placement signal source and calibrating the channel according to the known position of the placement signal source.

[0014] As a further limitation of the technical solution of this embodiment of the invention, the passive calibration is: acquiring known information in the radar echo signal and using the known information to perform calibration.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the step of obtaining the Doppler power spectrum involves extracting effective frequency points from the Doppler power spectrum: after the first FFT transformation and the second FFT transformation, the Doppler power spectrum is obtained, the Doppler power spectrum corresponds to a distance element, and the Doppler power spectrum contains all flow velocity information on the distance element.

[0016] As a further limitation of the technical solution of this invention, the step of performing multi-channel data processing and fuzzy mathematical inversion to estimate the azimuth angle from the effective frequency points specifically includes the following steps:

[0017] The received data of the radar array corresponding to the effective frequency point is decomposed into two mutually orthogonal subspaces.

[0018] Construct a spatial spectral function using the orthogonality of the two mutually orthogonal subspaces;

[0019] Based on the spatial spectral function, the azimuth angle is obtained through spectral peak search.

[0020] As a further limitation of the technical solution of this embodiment of the invention, the radar array is a uniform linear array.

[0021] As a further limitation of the technical solution of this embodiment of the invention, in the uniform linear array, the number of channels of the array is N, the interval between the arrays is d, the far-field narrowband signal is incident on the uniform linear array, the incident angle of the far-field narrowband signal is θ, and the first channel is taken as the reference channel, then the phase difference between the l-th channel and the reference channel is:

[0022] ;

[0023] Where λ is the wavelength of the radar electromagnetic wave;

[0024] For a uniform linear array with N channels, the phase difference between all channels and the reference channel is expressed as:

[0025] ;

[0026] For M incident signals from different directions simultaneously incident on the uniform linear array, the incident angle is:

[0027] .

[0028] A multi-channel data processing system for river flow measurement side-scan radar, the system comprising a first transform solution range unit, a second transform solution velocity unit, an effective frequency point extraction unit, and a multi-channel inversion angle estimation unit, wherein:

[0029] The first transform range unit is used to perform the first FFT transform on the radar echo signal to solve the range.

[0030] The second transform velocity unit is used to perform a second FFT transform velocity solution on the radar echo signal;

[0031] An effective frequency extraction unit is used to acquire the Doppler power spectrum and extract the effective frequency points from the Doppler power spectrum.

[0032] The multi-channel inversion angle estimation unit is used to perform multi-channel data processing and fuzzy mathematical inversion angle estimation on the effective frequency points to obtain the azimuth angle.

[0033] As a further limitation of the technical solution of this embodiment of the invention, the multi-channel inversion angle estimation unit specifically includes:

[0034] The feature decomposition module is used to perform feature decomposition on the received data of the radar array corresponding to the effective frequency point to obtain two mutually orthogonal subspaces.

[0035] A spatial spectrum function construction module is used to construct a spatial spectrum function using the orthogonality of the two mutually orthogonal subspaces;

[0036] The spectral peak search module is used to obtain the orientation based on the spatial spectral function through spectral peak search.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] This invention describes a method for obtaining range information by performing a first FFT transform on the radar echo signal, followed by a second FFT transform to obtain velocity information. The Doppler power spectrum is then acquired, and effective frequency points are extracted from it. Finally, multi-channel data processing (fuzzy mathematical inversion) is used to estimate the azimuth angle for each effective frequency point. This method allows for the acquisition of range and velocity information through two FFT transforms, combined with multi-channel data processing (fuzzy mathematical inversion) to estimate the azimuth angle for each effective frequency point. This results in a flow field where range, velocity, and azimuth are all individually correlated, ensuring that each radial velocity in the flow field simultaneously possesses range, velocity, and azimuth information. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0040] Figure 1 A schematic diagram illustrating the interaction between water waves and electromagnetic waves in this invention is shown.

[0041] Figure 2 A flowchart of the method provided by an embodiment of the present invention is shown.

[0042] Figure 3 A schematic diagram of the multi-channel technology in this invention is shown.

[0043] Figure 4 An application architecture diagram of the system provided in an embodiment of the present invention is shown. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0045] It is understandable that water ripples traveling radially towards or away from the antenna will resonate when their wavelength is exactly half the radar wavelength, resulting in the strongest backscattering. Moreover, this resonance is independent of wind speed and water surface conditions. The backscattering mechanism of waves was explained using the Bragg diffraction theory of crystals. Figure 1 This diagram illustrates the interaction between water waves and electromagnetic waves in this invention. Blue waves represent water waves, black waves represent electromagnetic waves, L is the wavelength of the water wave, λ is the wavelength of the radar electromagnetic wave, and θ is the angle between the electromagnetic wave and the water wave. When L, λ, and θ satisfy the relationship Lcos(θ) = λ / 2, the phase difference between the backscattering of two adjacent wave crests in a wave train is 2π. Therefore, the backscattering of each wave crest of a wave train with wavelength L forms a phase-to-phase superposition, while the backscattering of other waves that do not satisfy this relationship cannot achieve phase-to-phase superposition. Therefore, when a wave train of a specific frequency moving towards or away from the radar interacts with the radar electromagnetic wave, it produces a strong backscattering effect. Based on this theory, the radar can monitor the movement of waves at specific wavelengths.

[0046] In hydrological monitoring technology, the flow field detected by ultra-high frequency radar is a fan-shaped area. Each range cell is divided into multiple surface targets according to the range resolution and angular resolution. Although the velocity information of multiple surface targets at each range cell is obtained after processing, it is impossible to correspond each velocity to each surface target at that range cell.

[0047] To address the aforementioned issues, this invention employs a first FFT transform on the radar echo signal to determine the range; a second FFT transform on the radar echo signal to determine the velocity; acquisition of the Doppler power spectrum; extraction of effective frequency points from the Doppler power spectrum; and multi-channel data processing using fuzzy mathematical inversion to estimate the azimuth angle. By obtaining range and velocity information through two FFT transforms and combining this with multi-channel data processing using fuzzy mathematical inversion, the azimuth angle is estimated for each effective frequency point. This results in a flow field where range, velocity, and azimuth are all individually correlated, ensuring that each radial velocity in the flow field simultaneously possesses range, velocity, and azimuth information.

[0048] Figure 2 A flowchart of the method provided by an embodiment of the present invention is shown.

[0049] Specifically, a method for processing multi-channel data from a river current measurement side-scan radar includes the following steps:

[0050] Step S100: Perform the first FFT transform on the radar echo signal to solve for the range.

[0051] Step S200: Perform a second FFT transform on the radar echo signal to determine the velocity.

[0052] Step S300: Obtain the Doppler power spectrum and extract the effective frequency points from the Doppler power spectrum.

[0053] In this embodiment of the invention, after performing a first FFT transform on the radar echo signal to obtain the range and a second FFT transform to obtain the velocity, the Doppler power spectrum can be obtained. The Doppler power spectrum corresponds to a range cell, and the Doppler power spectrum contains all the flow velocity information on that range cell. The effective frequency point can be extracted from the Doppler power spectrum.

[0054] Step S400: Perform multi-channel data processing and fuzzy mathematical inversion to estimate the angle of the effective frequency points to obtain the azimuth angle.

[0055] In this embodiment of the invention, by performing feature decomposition on the received data of the radar array corresponding to the effective frequency point, two mutually orthogonal subspaces are obtained (where: one is the signal subspace and the other is the noise subspace orthogonal to the signal subspace). By utilizing the orthogonality of the two mutually orthogonal subspaces, a spatial spectrum function is constructed, and then based on the spatial spectrum function, the azimuth angle is obtained by spectral peak search.

[0056] It is understandable that ultra-high frequency side-scan radar uses Yagi antennas as both transmitting and receiving antennas. The receiving antenna is a uniform linear array; therefore, the radar array itself is also a uniform linear array. For example... Figure 3A schematic diagram of the multi-channel method in this invention is shown. In a uniform linear array, the number of channels is N, and the spacing between the arrays is d. A far-field narrowband signal is incident on the uniform linear array at an incident angle of θ. Taking the first channel as the reference channel, the phase difference between the l-th channel and the reference channel is:

[0057] ;

[0058] Where λ is the wavelength of the radar electromagnetic wave;

[0059] For a uniform linear array with N channels, the phase difference between all channels and the reference channel is expressed as:

[0060] ;

[0061] For M incident signals from different directions simultaneously incident on the uniform linear array, the incident angle is:

[0062] .

[0063] In another embodiment of the present invention, the angle estimation method of multi-channel data processing fuzzy mathematical inversion requires that the amplitude and phase of each receiving channel of the radar be consistent. Therefore, the channels must be calibrated before the angle estimation is performed. Thus, before performing multi-channel data processing fuzzy mathematical inversion angle estimation on the effective frequency points, active calibration or passive calibration is performed. Active calibration involves acquiring the placement signal source and calibrating the channel according to the known position of the placement signal source. Passive calibration involves acquiring known information (such as moving hard targets and Bragg frequency points) in the radar echo signal and using the known information for calibration.

[0064] Furthermore, Figure 4 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0065] In another preferred embodiment of the present invention, a multi-channel data processing system for river flow measurement side-scan radar includes:

[0066] The first transform range unit 100 is used to perform the first FFT transform to solve the range of the radar echo signal.

[0067] The second transform speed unit 200 is used to perform a second FFT transform speed on the radar echo signal.

[0068] The effective frequency extraction unit 300 is used to acquire the Doppler power spectrum and extract the effective frequency from the Doppler power spectrum.

[0069] In this embodiment of the invention, after performing a first FFT transform to obtain the range and a second FFT transform to obtain the velocity from the radar echo signal, the effective frequency extraction unit 300 can obtain the Doppler power spectrum. The Doppler power spectrum corresponds to a range element, and the Doppler power spectrum contains all the flow velocity information on that range element, and the effective frequency point can be extracted from the Doppler power spectrum.

[0070] The multi-channel inversion angle estimation unit 400 is used to perform multi-channel data processing and fuzzy mathematical inversion angle estimation on the effective frequency points to obtain the azimuth angle.

[0071] In this embodiment of the invention, the multi-channel inversion angle estimation unit 400 performs feature decomposition on the received data of the radar array corresponding to the effective frequency point to obtain two mutually orthogonal subspaces (where: one is the signal subspace and the other is the noise subspace orthogonal to the signal subspace). By utilizing the orthogonality of the two mutually orthogonal subspaces, a spatial spectrum function is constructed, and then the azimuth angle is obtained based on the spatial spectrum function through spectrum peak search.

[0072] Specifically, in the preferred embodiment provided by the present invention, the multi-channel inversion angle estimation unit 400 specifically includes:

[0073] The feature decomposition module is used to perform feature decomposition on the received data of the radar array corresponding to the effective frequency point to obtain two mutually orthogonal subspaces.

[0074] A spatial spectrum function construction module is used to construct a spatial spectrum function using the orthogonality of the two mutually orthogonal subspaces;

[0075] The spectral peak search module is used to obtain the azimuth angle based on the spatial spectral function through spectral peak search.

[0076] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0077] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0078] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0079] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A river flow measurement side-looking radar multi-channel data processing method, characterized in that, The method specifically comprises the following steps: performing first FFT transformation to resolve distance on the radar echo signal; performing second FFT transformation to resolve velocity on the radar echo signal; obtaining a Doppler power spectrum, and extracting effective frequency points from the Doppler power spectrum; performing multi-channel data processing fuzzy mathematics inversion to estimate the angle of the effective frequency points to obtain an azimuth angle; The method specifically comprises the following steps: performing characteristic decomposition on the received data of the radar array corresponding to the effective frequency points to obtain two mutually orthogonal subspaces; constructing a spatial spectrum function by using the orthogonality of the two mutually orthogonal subspaces; obtaining the azimuth angle by spectrum peak searching based on the spatial spectrum function; After the first FFT transformation and the second FFT transformation, the Doppler power spectrum corresponding to one range cell is obtained, and the Doppler power spectrum contains all flow rate information on the range cell; The radar array is a uniform linear array, the number of channels of the array is N, the interval between the arrays is d, a far-field narrowband signal is incident to the uniform linear array, the incident angle of the far-field narrowband signal is θ, and the phase difference between the first channel and the reference channel is: ； wherein λ is the wavelength of the radar electromagnetic wave For the uniform linear array with N channels, the phase difference between all channels and the reference channel is represented as: ； For M incident signals from different directions incident to the uniform linear array at the same time, the incident angle is: 。 2. The river gauging side-looking radar multi-channel data processing method according to claim 1, characterized in that, The method further comprises the following steps: Before the multi-channel data processing fuzzy mathematics inversion to estimate the angle of the effective frequency points, active calibration or passive calibration is performed.

3. The river gauging side-looking radar multi-channel data processing method according to claim 2, characterized in that, The active calibration is to obtain a placed signal source, and calibrate the channels according to the known position of the placed signal source.

4. The river gauging side-looking radar multi-channel data processing method according to claim 2, characterized in that, The passive calibration is to obtain known information in the radar echo signal, and calibrate by using the known information.

5. A river flow measurement side-looking radar multi-channel data processing system, applied to the river flow measurement side-looking radar multi-channel data processing method of any one of claims 1-4, characterized in that, The system comprises a first transformation range resolution unit, a second transformation velocity resolution unit, an effective frequency point extraction unit and a multi-channel inversion angle estimation unit, wherein: The first transformation range resolution unit is configured to perform first FFT transformation to resolve distance on the radar echo signal. The second transformation velocity resolution unit is configured to perform second FFT transformation to resolve velocity on the radar echo signal. The effective frequency point extraction unit is configured to obtain a Doppler power spectrum, and extract effective frequency points from the Doppler power spectrum. The multi-channel inversion angle estimation unit is configured to perform multi-channel data processing fuzzy mathematics inversion to estimate the angle of the effective frequency points to obtain an azimuth angle.

6. The river gauging side-looking radar multichannel data processing system according to claim 5, characterized in that, The multi-channel inversion angle estimation unit specifically comprises: A characteristic decomposition module configured to perform characteristic decomposition on the received data of the radar array corresponding to the effective frequency points to obtain two mutually orthogonal subspaces. A spatial spectrum function construction module configured to construct a spatial spectrum function by using the orthogonality of the two mutually orthogonal subspaces. A spectrum peak searching module configured to obtain the azimuth angle by spectrum peak searching based on the spatial spectrum function.

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