Radar static clutter suppression method and system
By dividing radar echo data into sub-apertures and performing virtual elevation phase shifting and circular fitting, the problem of static clutter interference in radar is solved, achieving effective suppression of static clutter and improved measurement accuracy.
Patent Information
- Application Number
- CN202511979859.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-02-06
AI Technical Summary
In millimeter-wave radar vibration measurement, clutter signals reflected by stationary objects within the radar field of view overlap with the target vibration signal, introducing additional phase noise and resulting in significant static clutter interference.
The original echo data is divided into multiple sub-apertures, and a virtual elevation phase offset is added to each sub-aperture. The global center and radius are determined by Fourier transform and least squares circle fitting, thereby suppressing static clutter.
It effectively reduces clutter residue rate, eliminates center offset introduced by static clutter, achieves interference suppression of radar static clutter, and improves measurement accuracy.
Smart Images
Figure CN121477136A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of radar data processing, and more particularly relates to a radar static clutter suppression method and system. BACKGROUND
[0002] A millimeter wave radar transmits a high-frequency electromagnetic wave signal and receives a reflected echo signal of a target, and based on phase information of the echo, vibration information of the target object is inversed. In recent years, due to its advantages of non-contact, high precision, small size, low power consumption and the like, it has shown great potential in large structure deformation monitoring, equipment vibration measurement, vital sign detection and the like. However, in millimeter wave radar vibration measurement, the clutter signal reflected by the stationary object in the radar field of view and the target vibration signal are superimposed, which introduces additional phase noise, and the static clutter interference of the radar is large.
[0003] Therefore, a radar static clutter suppression method is needed. SUMMARY
[0004] The application aims to provide a radar static clutter suppression method and system to eliminate the circle center offset introduced by the static clutter and realize interference suppression of the radar static clutter.
[0005] The first aspect of the embodiment of the application provides a radar static clutter suppression method, comprising: The original echo data is divided into sub-apertures to obtain a plurality of sub-apertures, and a virtual elevation phase offset is added to each sub-aperture to obtain phase offset echo data corresponding to each sub-aperture; the original echo data is data obtained by a target radar monitoring a target object; Based on the sub-aperture echo data, a range-azimuth Fourier transform is performed to obtain a phase amplitude information sequence of the signal; Based on the phase amplitude information sequence of each sub-aperture data signal, a stable scattering point is screened using an amplitude deviation index and combined with an amplitude threshold; the signal-to-noise ratio of the stable scattering point is greater than a preset signal-to-noise ratio threshold; For each sub-aperture, based on the range-azimuth Fourier transform of the echo data of the stable scattering point selected from the sub-aperture and the corresponding target radius, scaled complex data is determined; Based on the scaled complex data of each sub-aperture, it is mapped to a complex plane, and a least squares circle fitting is used to obtain a global circle center and a radius; Based on the global circle center, the radius and the scaling parameter, suppression clutter data corresponding to each sub-aperture is determined; Based on the suppression clutter data corresponding to each sub-aperture, a target deformation variable is determined.
[0006] The second aspect of the embodiment of the application provides a radar static clutter interference suppression system, comprising: The echo data segmentation module is used to divide the raw echo data into sub-apertures to obtain multiple sub-apertures, and to add a virtual elevation phase offset to each sub-aperture to obtain the phase offset echo data corresponding to each sub-aperture; the raw echo data is the data obtained by the target radar from the target object; The Fourier transform module is used to perform range-azimuth Fourier transform based on the echo data of each sub-aperture to obtain the phase amplitude information sequence of the signal. The filtering module is used to filter stable scattering points based on the phase amplitude information sequence of each sub-aperture data signal, using the amplitude deviation index and combined with the amplitude threshold; the signal-to-noise ratio of the stable scattering point is greater than the preset signal-to-noise ratio threshold. The scaling module is used to determine the scaled complex data for each sub-aperture based on the range-azimuth Fourier transform of the echo data from the stable scattering point selected for that sub-aperture and the corresponding target radius. The circle fitting module is used to map the scaled complex data of each sub-aperture onto the complex plane and obtain the global circle center and radius using least squares circle fitting. The clutter suppression determination module is used to determine the clutter suppression data corresponding to each sub-aperture based on the global center, radius, and scaling parameters. The deformation determination module is used to determine the target deformation based on the clutter suppression data corresponding to each sub-aperture.
[0007] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the radar static clutter suppression method described above.
[0008] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the radar static clutter suppression method described above.
[0009] The beneficial effects of the radar static clutter suppression method and system provided in this application are as follows: This application's embodiments divide the original echo data into multiple sub-apertures and add virtual elevation phase offsets to each sub-aperture to obtain phase-offset echo data. Then, by screening stable scattering points, the core feature carriers of static clutter are located. Furthermore, by scaling the data and fitting a circle, the global center and radius are obtained, unifying the scattered clutter features of the sub-apertures into a global model. This solves the problem of inconsistent clutter features in different observation areas and time samples. Based on the global center and radius, the clutter suppression data of each sub-aperture is inferred, which can cover the static clutter of the entire field of view, reduce the clutter residual rate, eliminate the center offset introduced by static clutter, and achieve interference suppression of radar static clutter. Attached Figure Description
[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0011] Figure 1 This is a schematic flowchart of a radar static clutter suppression method provided in an embodiment of this application; Figure 2 This is a schematic diagram of sub-aperture division provided in an embodiment of this application; Figure 3 A scatter plot of sub-aperture phase distribution without added phase offset is provided in one embodiment of this application; Figure 4 A scatter plot of the sub-aperture phase distribution after adding phase offset is provided in one embodiment of this application; Figure 5 A global circle fitting map provided in one embodiment of this application; Figure 6 This is a structural block diagram of a radar static clutter interference suppression system provided in an embodiment of this application; Figure 7 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation
[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.
[0013] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.
[0014] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a radar static clutter suppression method according to an embodiment of this application. The method can be executed by an electronic device, specifically a server or computer. The method may include steps S101-S108.
[0015] S101: Divide the raw echo data into sub-apertures to obtain multiple sub-apertures, and add a virtual elevation phase offset to each sub-aperture to obtain the phase offset echo data corresponding to each sub-aperture; the raw echo data is the data obtained by the target radar from monitoring the target object.
[0016] In this embodiment, raw echo data refers to the signal data reflected back by the target object (such as the ground, buildings, moving vehicles, etc.) after the target radar emits electromagnetic waves, including information such as the target's distance, azimuth, and speed. For example, cloud reflection signals received by weather radar and ground reflection signals received by synthetic aperture radar are both raw echo data. The target radar can be a multiple-input multiple-output synthetic aperture radar (MIMO-SAR).
[0017] In this embodiment, a sub-aperture is a block processing unit for the raw echo data. Since radar echo data typically contains a large number of temporal or spatial samples, such as multiple moments of continuous observation or multiple covered areas, directly processing the entire data is highly complex. Therefore, it is divided into multiple continuous, partially overlapping, or non-overlapping small segments, each segment being a sub-aperture. In this embodiment, by adding a virtual elevation phase offset to each sub-aperture, observation scenarios at different elevation angles can be simulated, adjusting the phase of the echo signal for each sub-aperture to make the phase characteristic differences between static clutter and dynamic targets more obvious, facilitating subsequent separation. Changing the signal phase simulates signals coming from different directions, enhancing signal diversity.
[0018] In this embodiment, the specific method for dividing the raw echo data into multiple sub-apertures can be found in [reference needed]. Figure 2 Divide the one-dimensional uniform linear array into A non-overlapping subarray.
[0019] S102: Perform range-azimuth Fourier transform on the echo data of each sub-aperture to obtain the phase amplitude information sequence of the signal.
[0020] In this embodiment, the phase-offset echo data refers to the sub-aperture echo data after virtual elevation phase offset processing. Fourier transform can convert a signal from the time domain (or spatial domain) to the frequency domain. In radar signal processing, its core function is to obtain the signal amplitude information sequence.
[0021] In this embodiment, azimuth refers to the lateral dimension of radar observation, that is, the horizontal direction perpendicular to the radar's flight direction (or observation direction). For example, when the radar flies in a north-south direction, the east-west direction is the azimuth, used to describe the target's left and right position on the horizontal plane. Range-azimuth frequency domain data refers to the frequency domain signal obtained in the range-azimuth dimension after performing a Fourier transform on the phase-shifted echo data. It reflects the frequency distribution characteristics of the echo signal in the range-azimuth direction, where: Static clutter (such as that from fixed buildings) typically has range-azimuth frequencies that are concentrated within a specific range and are stable. The range-azimuth frequency of dynamic targets (such as moving vehicles) may shift due to motion, exhibiting a different distribution pattern than clutter.
[0022] S103: Based on the phase amplitude information sequence of each sub-aperture data signal, stable scattering points are selected using the amplitude deviation index and combined with the amplitude threshold.
[0023] In this embodiment, a stable scattering point refers to a scattering point in the radar echo whose reflection characteristics (such as amplitude and phase) change little with time or observation conditions, corresponding to a static clutter source. The signal-to-noise ratio (SNR) of a stable scattering point is greater than a preset SNR threshold, which can be set empirically. Several stable scattering points are selected for each sub-aperture.
[0024] S104: For each sub-aperture, the scaled complex data is determined based on the range-azimuth Fourier transform of the echo data from the stable scattering point selected for that sub-aperture and the corresponding target radius.
[0025] In this embodiment, the scaled complex data is a quantization adjustment parameter for the clutter characteristics within the sub-aperture, used to match and normalize the range-azimuth data with the characteristics of stable scattering points. Specifically, the scaled data may include an amplitude scaling ratio and a phase offset. These parameters can be used to calibrate the clutter characteristics related to stable scattering points in the range-azimuth data to a uniform scale, eliminating feature differences between different sub-apertures (such as clutter amplitude / phase deviations caused by different observation angles and distances).
[0026] S105: Based on the scaled complex data of each sub-aperture, map it to the complex plane, and use least squares circle fitting to obtain the global center and radius.
[0027] In this embodiment, circle fitting is a data modeling method that uses mathematical calculations to fit multiple discrete data points into a circle, finding the circular parameters that best represent the distribution characteristics of these points. Here, the data points are the scaled data of each sub-aperture, which can be converted into coordinates on a two-dimensional plane, such as amplitude as the horizontal axis and phase as the vertical axis. The purpose of circle fitting is to extract global commonalities from the local clutter characteristics (scaled data) of multiple sub-apertures, that is, the characteristic model that all sub-aperture clutter follows.
[0028] In this embodiment, the global center is the coordinate of the center point of the circle obtained by circle fitting. It is a quantitative representation of the average trend of all sub-aperture scaling data and reflects the core benchmark of global clutter characteristics, such as average amplitude and average phase. The radius refers to the radius length of the circle obtained by circle fitting, reflecting the degree of dispersion of all sub-aperture scaling data around the global center, that is, the deviation range of different sub-aperture clutter characteristics from the global benchmark.
[0029] S106: Determine the clutter suppression data corresponding to each sub-aperture based on the global center, radius, and scaling parameters.
[0030] In this embodiment, clutter suppression data refers to data calculated for each sub-aperture to characterize the specific features of the clutter that needs to be suppressed in that sub-aperture, such as the amplitude range and phase interval of the clutter. The process of determining the clutter suppression data corresponding to each sub-aperture involves mapping the global clutter model (center and radius) to the local data of each sub-aperture, which may specifically include: Using the global center as a reference, the normal range of clutter characteristics is determined by combining the radius; for each sub-aperture, the signal characteristics belonging to clutter in that sub-aperture are calculated based on the relationship between its scaling data and the global model; finally, the clutter suppression data corresponding to each sub-aperture is obtained, ensuring the targeting and accuracy of clutter suppression.
[0031] S107: Determine the target deformation based on the clutter suppression data corresponding to each sub-aperture.
[0032] In this embodiment, the target deformation is a quantitative index used to characterize the overall deformation error caused by static clutter to the radar's raw echo data, that is, the measurement deviation caused by static clutter interference during the radar's acquisition of raw echo data.
[0033] In this embodiment, the clutter suppression data of each sub-aperture can be analyzed to extract the signal deformation characteristics caused by clutter, and then the results of all sub-apertures can be combined to obtain the global error quantification index. For example, the phase shift caused by clutter can be extracted from the clutter suppression data of each sub-aperture, and the statistical characteristics of these shifts (such as average value, weighted sum, etc.) can be calculated. The final overall shift is the target deformation.
[0034] As can be seen from the above, the embodiments of this application divide the original echo data into multiple sub-apertures and add virtual elevation phase offset to each sub-aperture to obtain phase offset echo data. Then, by screening stable scattering points, the core feature carrier of static clutter is located. Furthermore, by scaling the data and fitting the circle, the global center and radius are obtained, and the scattered clutter features of the sub-apertures are unified into a global model, which solves the problem of inconsistent clutter features in different observation areas and different time samples. Then, based on the global center and radius, the clutter suppression data of each sub-aperture is inferred, which can cover the static clutter of the entire field of view, reduce the clutter residual rate, eliminate the center offset introduced by static clutter, and achieve interference suppression of radar static clutter.
[0035] In one embodiment of this application, the method is characterized by adding a virtual elevation phase offset to each sub-aperture to obtain phase offset echo data corresponding to each sub-aperture, including: For each sub-aperture, a phase offset is added to the original sub-echo data corresponding to that sub-aperture based on the center position offset and virtual elevation angle to obtain the phase-offset echo data corresponding to that sub-aperture.
[0036] In this embodiment, the scatter plot of the signal distribution before applying the phase shift is as follows: Figure 3 As shown, the scatter plot of the phase distribution after applying the phase shift is as follows: Figure 4 As shown.
[0037] For each sub-aperture It can be offset from its center position. And virtual elevation angle The original sub-echo data corresponding to this sub-aperture Apply a phase shift based on the original phase:
[0038] in, Indicates phase shift, Let be the offset distance of the center of the k-th sub-aperture relative to the reference position. For radar wavelength, This is a virtual elevation angle.
[0039] The signal after applying the phase shift is: ,in This represents the offset echo data corresponding to the k-th sub-aperture. Indicates distance sampling, , Indicates the first Time sampling, Indicates azimuth sampling , Indicates the first Time sampling.
[0040] As can be seen from the above, considering that the scattering characteristics of static clutter (such as fixed objects) are stable, its phase is affected by the virtual elevation angle in a regular manner; while the phase of the target vibration signal changes dynamically with time, and its response to the virtual elevation angle is fundamentally different from that of clutter. Therefore, by applying phase shift, the separation between clutter points and target points is improved, which solves the problem of clutter and target aliasing in the original signal.
[0041] In one embodiment of this application, a range-azimuth Fourier transform is performed based on the echo data of each sub-aperture to obtain a sequence of phase and amplitude information of the signal, including: Perform a range-direction Fourier transform on the phase offset echo data corresponding to each sub-aperture to obtain the range-direction Fourier transform data for each sub-aperture. Perform an azimuth Fourier transform on the range Fourier transform data of each sub-aperture to obtain the phase amplitude information sequence corresponding to each sub-aperture.
[0042] In this embodiment, the specific mathematical formula for the distance-to-Fourier transform is as follows: Distance to Fourier Transform: ,in, This represents the Fourier transform data of the k-th sub-aperture in the range direction. This is range sampling, and 'a' is azimuth sampling. , Indicates the first Time sampling.
[0043] The specific mathematical formula for the azimuth-to-Fourier transform is as follows: Azimuth to Fourier Transform: ,in, This represents the Fourier transform data of the k-th sub-aperture in the azimuth direction. For distance sampling, Indicates the first Time sampling.
[0044] As can be seen from the above, in the embodiments of this application, the range-direction Fourier transform first converts the time-domain signal into range-direction frequency-domain data, focusing on the signal frequency characteristics at different distances, which can initially distinguish clutter and targets at near / far distances; then, the azimuth-direction Fourier transform is used to obtain azimuth-direction frequency-domain data, increasing the signal frequency difference at different lateral positions, making the distribution pattern of clutter and targets in the frequency domain clearer (such as clutter frequencies concentrated in a specific range, and target frequencies dispersed and dynamically changing), solving the problem of the two being difficult to distinguish in the time domain.
[0045] In one embodiment of this application, stable scattering points are screened using an amplitude deviation index combined with an amplitude threshold, based on the phase amplitude information sequence of each sub-aperture data signal, including: The range-azimuth frequency domain data corresponding to each sub-aperture is normalized to obtain the target range-azimuth frequency domain data for each sub-aperture; the target range-azimuth frequency domain data is the normalized range-azimuth frequency domain data. Calculate the amplitude dispersion index corresponding to the target range-azimuth frequency domain data for each sub-aperture; the amplitude dispersion index is used to characterize the amplitude stability of the target range-azimuth frequency domain data for each sub-aperture; wherein, the target range-azimuth frequency domain data for each sub-aperture contains multiple time sample data; For each sub-aperture, based on the amplitude dispersion index corresponding to the target range-azimuth frequency domain data and the amplitude threshold, stable scattering points corresponding to that sub-aperture are selected.
[0046] In this embodiment, the specific formula for normalization can be: ,in, This represents the Fourier transform data of the k-th sub-aperture in the azimuth direction. The normalized data is the target range-azimuth frequency domain data of the kth sub-aperture.
[0047] In this embodiment, the amplitude discrepancy index (ADI) is calculated using the following formula: ,in, Let be the standard deviation of the amplitude of the k-th sub-aperture. Let N be the mean amplitude of the k-th sub-aperture, and N be the number of time samples. This represents the number of sub-apertures. The criterion for determining a stable scattering point is... ,in This represents the distance-azimuth coordinates of the stable scattering point. The amplitude threshold can be determined empirically. In this embodiment, the stable scattering point can also be denoted as the data point corresponding to the permanent scatterer (PS).
[0048] As can be seen from the above, this embodiment of the application takes into account that the azimuth frequency domain data of different sub-apertures may have inconsistent amplitude absolute value scales due to differences in observation distance, reflection intensity, etc. Therefore, a normalization formula is used to uniformly map the data to the same interval, eliminating the interference of absolute intensity and making the amplitude stability characteristics of different sub-apertures comparable. This embodiment of the application quantifies the degree of signal fluctuation in multiple time samples by calculating the amplitude dispersion index, making the feature extraction of static clutter more objective and accurate.
[0049] In one embodiment of this application, determining the suppressed clutter data corresponding to each sub-aperture based on the global center, radius, and scaling parameters includes: Based on each sub-aperture, the global center and radius are inversely scaled according to the scaling parameters of that sub-aperture to obtain the target global center and target radius mapped to the original scale of that sub-aperture. For each sub-aperture, the clutter suppression data corresponding to that sub-aperture is determined based on the target global center, the target radius, and the scaling data corresponding to that sub-aperture.
[0050] In this embodiment, the echo data of each sub-aperture is... The result obtained after performing a range-azimuth FFT Record ,in This represents the number of sub-apertures, and the PS point (stable scattering point) selected for the k-th sub-aperture is denoted as... Let the target radius be... Then the scaling factor
[0051] Then calculate the scaled data for each sub-aperture. The calculation formula is as follows:
[0052] Next, the data from each sub-aperture are concatenated, and then a circle is fitted using the least squares method to determine the global center. ,radius The global circle obtained by fitting is as follows: Figure 5 As shown, the data points of different shapes or colors represent echo data of different sub-apertures, which are used to reflect the distribution on the complex plane.
[0053] In this embodiment, the fitted global center and radius are subjected to global parameter inverse scaling, as shown in the following formula: ,
[0054] Global center of the target obtained based on inverse scaling and target radius To suppress static clutter interference, the specific formula is as follows: ,in, This represents the suppressed clutter data corresponding to the k-th sub-aperture. Static clutter causes the center of the IQ signal distribution circle in the complex plane to deviate from the origin. This offset is eliminated by subtracting the center of the circle after inverse scaling, which is how clutter interference is suppressed.
[0055] As can be seen from the above, considering that the global center and radius obtained by the previous circle fitting are based on the scaled data and their scale differs from the original echo data, this embodiment uses the inverse scaling formula to convert the global center and radius back to the original signal scale, eliminating the parameter offset caused by the scaling process and eliminating the IQ center offset introduced by static clutter, thus achieving high-precision extraction of the target's minute vibrations.
[0056] In one embodiment of this application, determining the target deformation based on the clutter suppression data corresponding to each sub-aperture includes: For each sub-aperture corresponding to the suppressed clutter data, extract the phase sequence of the suppressed clutter data corresponding to that sub-aperture as a function of time samples; Calculate the phase difference of the suppressed clutter data corresponding to the sub-aperture in each adjacent time sample; The target deformation is determined based on the phase difference of the suppressed clutter data corresponding to each sub-aperture on their respective adjacent time samples.
[0057] In this embodiment, it can be Represented as Then, extract the phase: The phase difference operation between adjacent time points can be determined based on the following method: , Indicates the first There are n time samples, where n represents the time point.
[0058] In this embodiment, the target deformation is determined based on the phase difference of the suppressed clutter data corresponding to each sub-aperture in their respective adjacent time samples, including: For the phase difference of the suppressed clutter data corresponding to each sub-aperture in each adjacent time sample, each phase difference is constrained to the target interval to obtain multiple target phase differences; The deformation of the sub-aperture is determined based on the phase difference of multiple targets; The weighted calculation weights of the deformation variables corresponding to each sub-aperture are determined based on the signal-to-noise ratio corresponding to each sub-aperture, and the target deformation variables are obtained by weighted calculation based on the weighted calculation weights of the deformation variables corresponding to each sub-aperture and the deformation variables corresponding to each sub-aperture.
[0059] In this embodiment, the target interval can be Each phase difference is constrained to the target interval, resulting in multiple target phase differences, denoted as... Based on this, the deformation of each sub-aperture is calculated by summing the results. The calculation formula is as follows:
[0060] in, Indicates the number of sub-apertures. Indicates the starting point of the true phase.
[0061] Finally, the displacement results are weighted and averaged based on the signal-to-noise ratio (SNR) of each sub-aperture to obtain the final deformation. The calculation formula is as follows:
[0062] in, This represents the signal-to-noise ratio of the k-th sub-aperture.
[0063] As can be seen from the above, the interference of static clutter on radar echoes is mainly manifested in phase distortion, and the change of phase over time (differential) can directly reflect the dynamic characteristics of this distortion. In this embodiment, by extracting the phase of the suppressed clutter data at multiple time samples and calculating the phase difference between adjacent time samples, the fluctuation law of phase caused by clutter over time is captured. The phase difference of static clutter is usually stable and has a small range, while the phase difference of the target signal will show significant changes due to vibration. Compared with directly using static phase values, it is more effective in focusing on the dynamic error introduced by clutter. Different sub-apertures correspond to different regions of the radar field of view, and the clutter distribution and interference intensity are different. This embodiment obtains the deformation corresponding to each sub-aperture by summing the cumulative phase difference of the target, realizing the local quantification of clutter interference in different regions. The stronger the clutter in a region, the larger the cumulative deformation, and vice versa, thus solving the limitation that traditional global average error is difficult to reflect local differences. In this embodiment, the noise interference levels of clutter suppression data for different sub-apertures vary: data from high-SNR sub-apertures are less affected by noise, resulting in more reliable deformation calculations; data from low-SNR sub-apertures have larger errors and lower reliability. This embodiment uses a signal-to-noise ratio weighted average to ensure that the deformation of high-SNR sub-apertures accounts for a higher proportion of the global target deformation, while the proportion of low-SNR sub-apertures is lower. This effectively reduces the interference of low-quality data on the global error, making the final target deformation more representative of the true global level of clutter interference and providing a more reliable quantitative benchmark for clutter suppression based on this deformation.
[0064] Corresponding to the radar static clutter suppression method in the above embodiment, Figure 6This is a structural block diagram of a radar static clutter interference suppression system provided in one embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 6 The radar static clutter interference suppression system 20 includes: an echo data partitioning module 21, a Fourier transform module 22, a filtering module 23, a scaling module 24, a circle fitting module 25, a clutter suppression determination module 26, and a deformation determination module 27.
[0065] The echo data division module 21 is used to divide the original echo data into sub-apertures to obtain multiple sub-apertures, and to add a virtual elevation phase offset to each sub-aperture to obtain the phase offset echo data corresponding to each sub-aperture; the original echo data is the data obtained by the target radar from the target object. Fourier transform module 22 is used to perform range-azimuth Fourier transform based on the echo data of each sub-aperture to obtain the phase amplitude information sequence of the signal; The filtering module 23 is used to filter stable scattering points based on the phase amplitude information sequence of each sub-aperture data signal, using the amplitude deviation index and combined with the amplitude threshold; the signal-to-noise ratio of the stable scattering point is greater than the preset signal-to-noise ratio threshold. Scaling module 24 is used to determine the scaled complex data for each sub-aperture based on the range-azimuth Fourier transform of the echo data of the stable scattering point selected for that sub-aperture and the corresponding target radius; The circle fitting module 25 is used to map the scaled complex data of each sub-aperture onto the complex plane and obtain the global circle center and radius by least squares circle fitting. The clutter suppression determination module 26 is used to determine the clutter suppression data corresponding to each sub-aperture based on the global center, radius and scaling parameters; The deformation determination module 27 is used to determine the target deformation based on the suppression clutter data corresponding to each sub-aperture.
[0066] In one embodiment of this application, the echo data segmentation module 21 is specifically used to add a phase offset to the original sub-echo data corresponding to each sub-aperture based on the center position offset and virtual elevation angle corresponding to the sub-aperture, so as to obtain the phase-offset echo data corresponding to the sub-aperture.
[0067] In one embodiment of this application, the Fourier transform module 22 is specifically used to perform a range Fourier transform on the phase offset echo data corresponding to each sub-aperture to obtain the range Fourier transform data of each sub-aperture. Perform an azimuth Fourier transform on the range Fourier transform data of each sub-aperture to obtain the phase amplitude information sequence corresponding to each sub-aperture.
[0068] In one embodiment of this application, the filtering module 23 is specifically used to normalize the range-azimuth frequency domain data corresponding to each sub-aperture to obtain the target range-azimuth frequency domain data of each sub-aperture; the target range-azimuth frequency domain data is the normalized range-azimuth frequency domain data. Calculate the amplitude dispersion index corresponding to the target range-azimuth frequency domain data for each sub-aperture; the amplitude dispersion index is used to characterize the amplitude stability of the target range-azimuth frequency domain data for each sub-aperture; wherein, the target range-azimuth frequency domain data for each sub-aperture contains multiple time sample data; For each sub-aperture, based on the amplitude dispersion index corresponding to the target range-azimuth frequency domain data and the amplitude threshold, stable scattering points corresponding to that sub-aperture are selected.
[0069] In one embodiment of this application, the clutter suppression determination module 26 is specifically used to perform inverse scaling on the global center and radius based on each sub-aperture and according to the scaling parameter of the sub-aperture, to obtain the target global center and target radius mapped to the original scale of the sub-aperture; For each sub-aperture, the clutter suppression data corresponding to that sub-aperture is determined based on the target global center, the target radius, and the scaling data corresponding to that sub-aperture.
[0070] In one embodiment of this application, the deformation determination module 27 is specifically used to extract the phase sequence of the suppressed clutter data corresponding to each sub-aperture as a function of time samples for the suppressed clutter data corresponding to that sub-aperture. Calculate the phase difference of the suppressed clutter data corresponding to the sub-aperture in each adjacent time sample; The target deformation is determined based on the phase difference of the suppressed clutter data corresponding to each sub-aperture on their respective adjacent time samples.
[0071] In one embodiment of this application, the deformation determination module 27 is further used to constrain each phase difference to a target interval for the phase difference of the suppressed clutter data corresponding to each sub-aperture in each adjacent time sample, so as to obtain multiple target phase differences. The deformation of the sub-aperture is determined based on the phase difference of multiple targets; The weighted calculation weights of the deformation variables corresponding to each sub-aperture are determined based on the signal-to-noise ratio corresponding to each sub-aperture, and the target deformation variables are obtained by weighted calculation based on the weighted calculation weights of the deformation variables corresponding to each sub-aperture and the deformation variables corresponding to each sub-aperture.
[0072] See Figure 7 , Figure 7 This is a schematic block diagram of an electronic device provided according to an embodiment of this application.Figure 7 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of each module / unit in the above-described device embodiments, for example... Figure 6 The functions of the echo data partitioning module 21, Fourier transform module 22, filtering module 23, scaling module 24, circle fitting module 25, clutter suppression determination module 26, and deformation determination module 27 are shown.
[0073] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.
[0074] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.
[0075] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.
[0076] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation method described in the radar static clutter suppression method provided in the embodiments of this application, or they can execute the implementation method of the electronic device described in the embodiments of this application, which will not be repeated here.
[0077] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0078] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.
[0079] Those skilled in the art will recognize that the modules / units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.
[0080] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0081] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules, units, or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or modules / units, or it may be an electrical, mechanical, or other form of connection.
[0082] The modules / units described as separate components may or may not be physically separate. Similarly, the components shown as modules / units may or may not be physical modules / units; they may be located in one place or distributed across multiple network modules / units. Some or all of the modules / units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.
[0083] Furthermore, the functional modules / units in the various embodiments of this application can be integrated into one processing module / unit, or each module / unit can exist physically separately, or two or more modules / units can be integrated into one module / unit. The integrated modules / units described above can be implemented in hardware or in the form of software functional modules / units.
[0084] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for suppressing static clutter in radar, characterized in that, include: The original echo data is divided into sub-apertures to obtain multiple sub-apertures. A virtual elevation phase offset is added to each sub-aperture to obtain the phase offset echo data corresponding to each sub-aperture. The raw echo data is the data obtained by the target radar from monitoring the target object; Range-azimuth Fourier transform is performed on the echo data of each sub-aperture to obtain the phase amplitude information sequence of the signal; Based on the phase amplitude information sequence of each sub-aperture data signal, stable scattering points are screened using the amplitude deviation index combined with the amplitude threshold. The signal-to-noise ratio of the stable scattering point is greater than a preset signal-to-noise ratio threshold; For each sub-aperture, the scaled complex data is determined based on the range-azimuth Fourier transform of the echo data from the stable scattering point selected for that sub-aperture and the corresponding target radius; Based on the scaled complex data of each sub-aperture, it is mapped to the complex plane, and the global center and radius are obtained by least squares circle fitting. Based on the global center, radius, and scaling parameters, determine the clutter suppression data corresponding to each sub-aperture; The target deformation is determined based on the clutter suppression data corresponding to each sub-aperture.
2. The radar static clutter suppression method as described in claim 1, characterized in that, The process of adding a virtual elevation phase offset to each sub-aperture to obtain the phase offset echo data corresponding to each sub-aperture includes: For each sub-aperture, a phase offset is added to the original sub-echo data corresponding to that sub-aperture based on the center position offset and virtual elevation angle to obtain the phase-offset echo data corresponding to that sub-aperture.
3. The radar static clutter suppression method as described in claim 1, characterized in that, The step of performing a range-azimuth Fourier transform on the echo data from each sub-aperture to obtain a sequence of phase and amplitude information of the signal includes: Perform a range-direction Fourier transform on the phase offset echo data corresponding to each sub-aperture to obtain the range-direction Fourier transform data for each sub-aperture. Perform an azimuth Fourier transform on the range Fourier transform data of each sub-aperture to obtain the phase amplitude information sequence corresponding to each sub-aperture.
4. The radar static clutter suppression method as described in claim 1, characterized in that, The phase amplitude information sequence based on the data signal of each sub-aperture, using the amplitude deviation index and combined with the amplitude threshold to screen stable scattering points, includes: The range-azimuth frequency domain data corresponding to each sub-aperture is normalized to obtain the target range-azimuth frequency domain data for each sub-aperture; the target range-azimuth frequency domain data is the normalized range-azimuth frequency domain data. Calculate the amplitude dispersion index corresponding to the target range-azimuth frequency domain data for each sub-aperture; the amplitude dispersion index is used to characterize the amplitude stability of the target range-azimuth frequency domain data for each sub-aperture; wherein, the target range-azimuth frequency domain data for each sub-aperture contains multiple time sample data; For each sub-aperture, based on the amplitude dispersion index corresponding to the target range-azimuth frequency domain data and the amplitude threshold, stable scattering points corresponding to that sub-aperture are selected.
5. The radar static clutter suppression method as described in claim 1, characterized in that, The determination of the clutter suppression data corresponding to each sub-aperture based on the global center, radius, and scaling parameters includes: Based on each sub-aperture, the global center and radius are inversely scaled according to the scaling parameters of the sub-aperture to obtain the target global center and target radius mapped to the original scale of the sub-aperture. For each sub-aperture, the clutter suppression data corresponding to that sub-aperture is determined based on the target global center, the target radius, and the scaling data corresponding to that sub-aperture.
6. The radar static clutter suppression method as described in claim 1, characterized in that, The determination of the target deformation based on the clutter suppression data corresponding to each sub-aperture includes: For each sub-aperture corresponding to the suppressed clutter data, extract the phase sequence of the suppressed clutter data corresponding to that sub-aperture as a function of time samples; Calculate the phase difference of the suppressed clutter data corresponding to the sub-aperture in each adjacent time sample; The target deformation is determined based on the phase difference of the suppressed clutter data corresponding to each sub-aperture on their respective adjacent time samples.
7. The radar static clutter suppression method as described in claim 6, characterized in that, The determination of the target deformation based on the phase difference of the suppressed clutter data corresponding to each sub-aperture in their respective adjacent time samples includes: For the phase difference of the suppressed clutter data corresponding to each sub-aperture in each adjacent time sample, each phase difference is constrained to the target interval to obtain multiple target phase differences; The deformation corresponding to the sub-aperture is determined based on the phase difference of the multiple targets; The weighted calculation weights of the deformation variables corresponding to each sub-aperture are determined based on the signal-to-noise ratio corresponding to the sub-aperture, and the target deformation variables are obtained by weighted calculation based on the weighted calculation weights of the deformation variables corresponding to each sub-aperture and the deformation variables corresponding to each sub-aperture.
8. A radar static clutter interference suppression system, characterized in that, include: The echo data partitioning module is used to divide the original echo data into sub-apertures to obtain multiple sub-apertures, and to add a virtual elevation phase offset to each sub-aperture to obtain the phase offset echo data corresponding to each sub-aperture. The raw echo data is the data obtained by the target radar from monitoring the target object; The Fourier transform module is used to perform range-azimuth Fourier transform based on the echo data of each sub-aperture to obtain the phase amplitude information sequence of the signal. The filtering module is used to filter stable scattering points based on the phase amplitude information sequence of each sub-aperture data signal, using the amplitude deviation index and combined with the amplitude threshold. The signal-to-noise ratio of the stable scattering point is greater than a preset signal-to-noise ratio threshold; The scaling module is used to determine the scaled complex data for each sub-aperture based on the range-azimuth Fourier transform of the echo data from the stable scattering point selected for that sub-aperture and the corresponding target radius. The circle fitting module is used to map the scaled complex data of each sub-aperture onto the complex plane and obtain the global circle center and radius using least squares circle fitting. The clutter suppression determination module is used to determine the clutter suppression data corresponding to each sub-aperture based on the global center, radius, and scaling parameters. The deformation determination module is used to determine the target deformation based on the clutter suppression data corresponding to each sub-aperture.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.