Method and apparatus for clutter suppression

Abstract

The application provides a sensing clutter suppression method and device, and relates to the technical field of communication, and the method comprises the following steps: acquiring echo signals collected by multiple sensing base stations arranged in a spatial diversity manner, wherein the echo signals comprise sensing target reflection signals and clutter interference signals; determining whether each signal point in the echo signals collected by the multiple sensing base stations satisfies spatial consistency; determining sensing target signal points and clutter signal points according to whether each signal point satisfies spatial consistency; and performing sensing clutter suppression according to the clutter signal points. The method provided by the application can efficiently suppress the clutter in a complex environment, and significantly improves the target detection probability in a complex urban environment.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a method and apparatus for sensing clutter suppression. Background Technology

[0002] In practical applications of integrated sensing and communication systems, clutter interference is a core issue limiting target detection performance. In complex urban environments, dense high-rise buildings and severe obstruction create diverse and complex electromagnetic radiation, posing a significant challenge to integrated sensing and communication systems and hindering their target perception performance.

[0003] In related technologies, traditional anti-clutter techniques mainly include monostation moving target display, moving target detection, and pulse Doppler filtering. These techniques suppress clutter by utilizing the difference in motion characteristics between the target and clutter. However, static clutter from buildings has a high amplitude, and the phase noise it carries is random, resulting in spectral spread in the Doppler spectrum. The energy is not concentrated at zero frequency, making it impossible to eliminate using traditional methods. This affects the detection effect of weak targets and the ability to suppress clutter. Summary of the Invention

[0004] This invention provides a method and apparatus for suppressing sensing clutter. By deploying multi-view sensing base stations with spatial diversity, it collects differentiated echo signals and utilizes the spatial consistency of the reflected signals from real sensed targets and the spatial dependence of clutter signals to achieve precise separation of target signal points and clutter signal points. This enables efficient suppression of clutter in complex environments, overcoming the limitations of traditional single-station clutter suppression and effectively suppressing static clutter from buildings and spectral spread clutter. It also significantly improves the target detection probability in complex urban environments, greatly reduces the false alarm rate, and has the advantages of strong environmental adaptability and controllable engineering deployment costs, ensuring the reliability and real-time performance of the sensing system in dense urban scenarios.

[0005] This invention provides a method for sensing clutter suppression, comprising the following steps: The echo signals collected by multiple sensing base stations deployed in spatial diversity are acquired, and the echo signals include the reflected signals of the sensing target and the clutter interference signals. Based on the location information of each signal point in the echo signal collected by each sensing base station, determine whether the spatial consistency is satisfied among the signal points of multiple echo signals. Based on whether the spatial consistency is satisfied among the various signal points, the target signal points and clutter signal points are determined; Based on the clutter signal points, sensing clutter suppression is performed.

[0006] According to a sensing clutter suppression method provided by the present invention, before determining whether spatial consistency is satisfied among the signal points of multiple echo signals based on the position information of each signal point in the echo signals collected by each sensing base station, the method further includes: Spatiotemporal consistency calibration is performed on the echo signals collected by each sensing base station.

[0007] According to a sensing clutter suppression method provided by the present invention, when the spatial position deviation between signal points is less than a first threshold and the time deviation is less than a second threshold, it is determined that the spatial consistency between signal points is satisfied.

[0008] According to a clutter suppression method provided by the present invention, determining the target signal point and clutter signal point based on whether the signal points satisfy spatial consistency includes: Signal points that satisfy spatial consistency as well as motion consistency, polarization consistency and amplitude consistency are identified as sensing target signal points. Signal points that do not meet spatial consistency, or that meet spatial consistency but do not meet the aforementioned motion consistency, polarization consistency, and amplitude consistency, are identified as clutter signal points.

[0009] According to a sensing clutter suppression method provided by the present invention, when the velocity deviation between signal points is less than a third threshold and the heading deviation is less than a fourth threshold, it is determined that the motion consistency between signal points is satisfied. If the polarization ratio deviation between signal points is less than the fifth threshold, it is determined that the polarization consistency between signal points is satisfied. If the amplitude fluctuation between signal points is less than the sixth threshold, it is determined that the signals meet the amplitude consistency requirement.

[0010] According to a sensing clutter suppression method provided by the present invention, the step of sensing clutter suppression based on the clutter signal point includes: Given that the distribution characteristics of the clutter signal points conform to a normal distribution, a minimum mean square error adaptive filtering method is used to suppress sensing clutter.

[0011] According to a sensing clutter suppression method provided by the present invention, after performing sensing clutter suppression using a minimum mean square error adaptive filtering method, the method further includes: Determine the clutter suppression ratio; If the clutter suppression ratio is less than the seventh threshold, the filter coefficients in the minimum mean square error adaptive filtering method are updated, and then clutter suppression is performed until the clutter suppression ratio is greater than the seventh threshold.

[0012] The present invention also provides a sensing clutter suppression device, comprising the following modules: The acquisition module is used to acquire echo signals collected by multiple sensing base stations deployed in spatial diversity, wherein the echo signals include the reflected signals of the sensing target and clutter interference signals. The first determining module is used to determine whether the spatial consistency between the signal points of multiple echo signals is satisfied based on the position information of each signal point in the echo signals collected by each sensing base station. The second determining module is used to determine the sensing target signal point and clutter signal point based on whether the spatial consistency between the various signal points is satisfied. The suppression module is used to perform sensing clutter suppression based on the clutter signal points.

[0013] The present invention also provides a network-side device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the sensing clutter suppression method as described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the sensing clutter suppression method as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the sensing clutter suppression method as described above.

[0016] The sensing clutter suppression method and apparatus provided by this invention collects differentiated echo signals by deploying multi-view sensing base stations in a spatial diversity manner. By utilizing the spatial consistency of the reflected signals from real sensing targets and the spatial dependence of clutter signals, the method achieves accurate separation of target signal points and clutter signal points, thus realizing efficient clutter suppression in complex environments. This not only breaks through the limitations of traditional single-station clutter suppression and effectively suppresses static clutter from buildings and spectral spread clutter, but also significantly improves the target detection probability in complex urban environments and greatly reduces the false alarm rate. At the same time, it has the advantages of strong environmental adaptability and controllable engineering deployment costs, ensuring the detection reliability and real-time performance of the sensing system in dense urban scenarios. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is one of the flowcharts of the sensing clutter suppression method provided by the present invention.

[0019] Figure 2 This is a schematic diagram of the sensing clutter suppression device provided by the present invention.

[0020] Figure 3 This is a schematic diagram of the network-side device provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] The following is combined Figures 1 to 3 The present invention describes a method and apparatus for suppressing sensing clutter.

[0023] To facilitate a clearer understanding of the technical solutions of the various embodiments of this application, some technical content related to the various embodiments of this application will be introduced first.

[0024] In practical applications of integrated sensing systems, clutter interference is a core issue limiting target detection performance. In complex urban environments, dense high-rise buildings and severe obstruction, coupled with diverse and complex electromagnetic radiation, pose significant clutter interference challenges to integrated sensing systems, hindering target perception performance. Clutter refers to reflected signals generated after the sensing signal illuminates non-target objects (such as the ground, tall buildings, trees, clouds, and rain), and its intensity is often much greater than the target signal. In densely populated urban areas, stationary targets such as buildings can cause fixed reflected echoes in the R-spectrum during RV processing of the received echo signals from the sensing base station. This raises the noise floor, leading to clutter masking the target in the sensing system. This manifests as low signal-to-noise ratio, high false alarm rate, high false alarm rate, and poor positioning accuracy.

[0025] Currently, traditional anti-clutter technologies mainly include monostation moving target indication (MTI), moving target detection (MTD), and pulse Doppler (PD) filtering. These technologies achieve clutter suppression by utilizing the differences in motion characteristics between the target and clutter (such as target motion speed and Doppler frequency shift).

[0026] Building static clutter is stationary and can be eliminated by static clutter cancellation before velocity-dimensional FFT. However, building static clutter has a high amplitude, and the phase noise it carries is random, resulting in spectral expansion on the Doppler spectrum (V spectrum). The energy is not concentrated at zero frequency, so it cannot be eliminated by traditional methods, affecting the detection effect of weak targets and the ability to suppress clutter cancellation.

[0027] In complex and densely populated urban areas, existing technologies have significant limitations: 1. Fixed viewing angle leads to unavoidable clutter: The detection viewing angle (azimuth and elevation angle) of a single sensor station is fixed. If the target is in a strong clutter area (such as in front of a cluster of tall buildings), the clutter signal and its non-zero clutter components after spectral expansion will completely mask the target signal. Even if MTI / MTD filtering is used, it can only filter out zero-frequency clutter signals and cannot identify and filter out non-zero-frequency clutter components. The high-energy non-zero-frequency clutter components will mask the real signal of the UAV target, resulting in serious missed detection problems. 2. Insufficient performance in multi-target scenarios: Although multi-site networking is adopted, there is a lack of intelligent collaboration mechanisms, and data fusion is limited to logical "AND / OR" processing. When multiple targets are simultaneously in cluttered areas, traditional technologies struggle to distinguish the reflected signals of different targets, resulting in high false alarm rates and severe target loss. 3. Limited ability to suppress static clutter: The amplitude and spectrum of static clutter from buildings are high, making it difficult for traditional methods to effectively separate clutter from the target signal.

[0028] Figure 1 This is one of the flowcharts illustrating the sensing clutter suppression method provided by the present invention, such as... Figure 1 As shown, the method includes the following: Step 101: Obtain echo signals collected by multiple sensing base stations deployed in spatial diversity. The echo signals include the reflected signals from the sensing target and clutter interference signals.

[0029] Specifically, in this embodiment, multiple sensing base stations are deployed within the sensing area according to the principle of complementary line-of-sight. Each sensing base station independently transmits sensing waveforms and collects echo data containing reflected signals from the sensing target and clutter interference signals. Optionally, due to the different locations of each sensing base station, the clutter signals received by them have certain spatial differences, while the actual reflected signals from the sensing target have strong consistency.

[0030] For example, within a designated sensing area, M sensing base stations (M≥2) are deployed according to the principle of complementary line-of-sight. The distance between each base station must meet the inter-station spacing requirements, and the base station distribution must cover different viewing angles within the sensing area to ensure that the same clutter source presents differentiated signal strengths from different station perspectives. Optionally, the multiple sensing base stations deployed in spatial diversity include, but are not limited to, networking with individual low-frequency equipment, networking with individual high-frequency equipment, and joint networking with low-frequency and high-frequency equipment. Among them, the mid-frequency equipment is deployed as a 4.9GHz integrated sensing device, which has a long coverage distance and strong resistance to rain attenuation, making it suitable for deployment in the outer warning zone to detect targets at greater distances; the high-frequency equipment is a 26GHz millimeter-wave integrated sensing device, which has a narrower beamwidth, slightly better positioning accuracy, and slightly stronger anti-interference capability, making it suitable for deployment in the inner core area to focus on tracking targets. Optionally, this application utilizes different stations to collect echo signals, where clutter is typically randomly distributed in space and its intensity varies with location; while the reflected signal of the real sensing target has strong continuity and consistency in space, and can be identified through joint analysis of multi-station data even in complex clutter backgrounds.

[0031] Step 102: Based on the location information of each signal point in the echo signal collected by each sensing base station, determine whether the spatial consistency is satisfied among the signal points of multiple echo signals.

[0032] Specifically, after acquiring the echo signals collected by multiple sensing base stations deployed in spatial diversity, this application can determine whether the signal points collected by different sensing base stations satisfy spatial consistency based on the position information of each signal point in the echo signals. Optionally, when the spatial position deviation between signal point A in the echo signal collected by sensing base station X and signal point B in the echo signal collected by sensing base station Y is less than a first threshold (e.g., ≤20m) and the time deviation is less than a second threshold (e.g., ≤1μs), then signal point A and signal point B are determined to satisfy spatial consistency. Otherwise, signal point A and signal point B are determined not to satisfy spatial consistency.

[0033] Step 103: Determine the target signal point and clutter signal point based on whether the spatial consistency between each signal point is satisfied.

[0034] Specifically, after determining whether the spatial consistency between the signal points of multiple echo signals is satisfied based on the location information of each signal point in the echo signals collected by each sensing base station, the signal points that satisfy the spatial consistency can be identified as the sensing target signal points, while the signal points that do not satisfy the spatial consistency can be identified as clutter signal points.

[0035] In other words, this application utilizes the difference between the spatial dependence of clutter intensity and the spatial consistency of real targets. By analyzing the spatial correlation of multi-station data, it can effectively distinguish clutter and real target signals, improve the target detection probability, reduce the false alarm rate, and at the same time ensure the real-time performance and reliability of the system.

[0036] Step 104: Based on the clutter signal points, perform sensing clutter suppression.

[0037] Specifically, after determining the target signal point and clutter signal point based on whether the spatial consistency between each signal point is met, this application can also achieve efficient suppression of clutter in complex environments, breaking through the limitations of traditional single-station clutter suppression and effectively suppressing building static clutter and spectrum spread clutter.

[0038] The method described above uses spatial diversity deployment of multi-view sensing base stations to collect differentiated echo signals. By utilizing the spatial consistency of the reflected signals from real-world targets and the spatial dependence of clutter signals, it achieves precise separation of target signal points and clutter signal points, thus realizing efficient suppression of clutter in complex environments. This not only overcomes the limitations of traditional single-station clutter suppression, effectively suppressing static clutter from buildings and spectral spread clutter, but also significantly improves the target detection probability in complex urban environments, greatly reducing the false alarm rate. At the same time, it has the advantages of strong environmental adaptability and controllable engineering deployment costs, ensuring the detection reliability and real-time performance of the sensing system in dense urban scenarios.

[0039] In some embodiments, before determining whether spatial consistency is satisfied among the signal points of multiple echo signals based on the location information of each signal point in the echo signals collected by each sensing base station, the method further includes: Spatiotemporal consistency calibration is performed on the echo signals collected by each sensing base station.

[0040] Specifically, in this embodiment, before determining whether the spatial consistency of each signal point in the echo signals collected by each sensing base station is satisfied based on the position information of each signal point in the echo signals collected by each sensing base station, spatiotemporal consistency calibration can be performed on the echo signals collected by each sensing base station to eliminate interference caused by equipment deployment and synchronization errors, and accurately achieve precise separation of the target and clutter. Optionally, an air interface synchronization scheme can be adopted to calibrate the transmit and receive synchronization delay of multiple sensing base stations deployed in spatial diversity to ≤3ns, ensuring that the time error of the echo signals collected by multiple stations is controlled within a very small range, so that the signal collection time of different base stations for the same target is consistent. Optionally, the high-precision AISU module of the integrated sensing base station can be used to pre-calibrate the latitude and longitude, altitude, antenna downtilt angle, antenna azimuth angle and other parameters of each base station, establish a unified global coordinate system, and convert the local polar coordinate signal data collected by each base station into rectangular coordinate data in the global coordinate system, ensuring that the coordinate transformation error is ≤10cm, and ensuring that the data of the same point in space at the same time can be compared and matched.

[0041] The method described above performs spatiotemporal consistency calibration on the echo signals collected by each sensing base station, effectively eliminating error interference such as time asynchrony and coordinate inconsistency caused by multi-station deployment, making the echo signals collected by different base stations comparable, avoiding misjudging the real target signal as clutter due to spatiotemporal deviation, and greatly improving the accuracy of target and clutter separation.

[0042] In some embodiments, if the spatial positional deviation between signal points is less than a first threshold and the time deviation is less than a second threshold, it is determined that the signal points satisfy spatial consistency.

[0043] Specifically, in this embodiment, the spatial position and time deviations of signal points collected by different base stations are calculated to achieve accurate determination of spatial consistency between signal points. That is, this application utilizes the spatial consistency of the real target signal and the spatial randomness of clutter signals to identify signal points that appear synchronously across stations as target signals, and to mark isolated, spatiotemporally asynchronous signal points as clutter signal points. This achieves accurate determination of target signal points and clutter signal points, improves target detection probability, and reduces false alarm rate.

[0044] For example, if the spatial position deviation between signal point A in the echo signal collected by sensing base station X and signal point B in the echo signal collected by sensing base station Y is less than a first threshold (e.g., ≤20m) and the temporal deviation is less than a second threshold (e.g., ≤1μs), then signal point A and signal point B are determined to satisfy spatial consistency. Otherwise, signal point A and signal point B are determined not to satisfy spatial consistency.

[0045] Optionally, the trajectory of a suspected moving target sensed by the base station with the highest SCR can be used as a benchmark, and signal points with spatial location deviation ≤20m, time deviation ≤1μs, and velocity ≥2m / s can be searched in the datasets of other stations as target signal points. Optionally, if a point exists only at a single station and has a velocity <2m / s, it is determined to be a clutter signal point, and the location of that station is marked and the clutter information is preserved.

[0046] The method described in the above embodiments fully utilizes the difference between the spatial consistency of the real target signal and the spatial randomness of the clutter signal. Signal points that meet the spatial consistency are identified as target signal points, while signal points that do not meet the spatial consistency are identified as clutter signal points. This achieves efficient separation of target signal points and clutter signal points, effectively reducing target misjudgment and clutter omission.

[0047] In some embodiments, determining the target sensing signal point and clutter signal point based on whether spatial consistency is satisfied among the various signal points includes: Signal points that satisfy spatial consistency as well as motion consistency, polarization consistency and amplitude consistency are identified as sensing target signal points. Signal points that do not meet spatial consistency, or that meet spatial consistency but not motion consistency, polarization consistency, and amplitude consistency, are identified as clutter signal points.

[0048] Specifically, in this embodiment, the target signal point and clutter signal point are accurately separated by the differences in spatial characteristics, motion characteristics, polarization characteristics, and amplitude characteristics between them. This solves the problem that traditional technologies cannot filter non-zero frequency clutter by relying solely on motion characteristics. Optionally, if the velocity deviation between signal points is less than 1 m / s and the heading deviation is less than 5°, the signal points are determined to satisfy motion consistency; if the polarization ratio deviation between signal points is less than 1.5, the signal points are determined to satisfy polarization consistency; and if the amplitude fluctuation between signal points is less than 5%, the signal points are determined to satisfy amplitude consistency. Optionally, signal points that satisfy spatial consistency and simultaneously satisfy motion consistency, polarization consistency, and amplitude consistency are determined as target signal points; signal points that do not satisfy spatial consistency, or satisfy spatial consistency but not motion consistency, polarization consistency, and amplitude consistency, are determined as clutter signal points.

[0049] The method described above, by sensing the differences between the target and clutter in multiple dimensions such as space, motion, polarization, and amplitude, that is, the spatial, motion, polarization, and amplitude characteristics of the real target are uniform and stable, while the characteristics of clutter are random and discrete, achieves accurate separation of target signal points and clutter signal points. This effectively solves the problems of difficult filtering of non-zero frequency clutter, high target misjudgment and high clutter miss rate, significantly improves the target detection probability in complex urban environments, and greatly reduces the false alarm rate.

[0050] In some embodiments, sensing clutter suppression is performed based on clutter signal points, including: Given that the distribution characteristics of clutter signal points conform to a normal distribution, a minimum mean square error adaptive filtering method is used to suppress sensing clutter.

[0051] Specifically, in this embodiment, for the selected clutter signal points, key parameters such as amplitude and variance are extracted. The maximum likelihood estimation method is used to analyze the distribution characteristics. If the distribution of the clutter signal points has a good fit to a normal distribution ≥ 0.95, with a mean amplitude between -12dB and -8dB and a variance between 4dB and 6dB, then it is determined that it conforms to the characteristics of a normal distribution. Optionally, if the selected clutter signal points are determined to conform to the characteristics of a normal distribution, the separability of the clutter can be confirmed by calculating the multi-station clutter covariance matrix. Then, the minimum mean square error (LMS) adaptive filtering algorithm is used to cancel the clutter echo signal and output the target signal.

[0052] For example, this application considers that static building clutter is the most significant fixed clutter interference source in low-altitude detection in dense urban areas, and its essence is the echo signal formed by electromagnetic waves reflected from buildings. The physical characteristics of this type of clutter highly match the mathematical characteristics of the log-normal distribution, and the clutter type can be identified through the characteristic distribution mapping relationship. Optionally, a probability density function of the log-normal distribution of clutter physical characteristics can be established: in, The probability density function representing the log-normal distribution of the physical properties of clutter. This represents the amplitude value of the clutter. Here, it represents the mean of the natural logarithm of the building static clutter amplitude. Here, the standard deviation of the natural logarithm of the building static clutter amplitude is represented.

[0053] Optionally, the distribution characteristics of suspected clutter information points can be statistically analyzed using the maximum likelihood estimation method. If the signal conforms to a log-normal distribution with a goodness of fit ≥0.95, the mean amplitude is between -12dB and -8dB, and the variance is between 4dB and 6dB, then the signal can be determined to be building static clutter, and the clutter signal can be saved as follows: Optionally, the clutter information of all stations within the multi-station collaborative system can be statistically analyzed sequentially and marked as follows: A database of building clutter elevation and azimuth angles within the detection area is constructed, and static clutter areas are marked and reported to the base station network management system.

[0054] Optionally, the clutter covariance matrix of the N sensing base stations can be calculated: in, Represents the clutter covariance matrix. This represents the total number of sensor base stations deployed in spatial diversity. Indicates the first clutter signal vectors collected by each base station This represents the conjugate transpose. When the difference in matrix eigenvalues ​​is ≥25dB, the clutter is considered separable.

[0055] Optionally, a minimum mean square error adaptive filtering method can be used to cancel clutter signals, assuming the first... The echo signal of each base station is ( Then, the output signal after filtering the clutter-cancelled signal is: in, Indicates the first The clean target signal after clutter cancellation at each base station Indicates the first The original echo signal of each base station for The filter coefficients at time 1. This indicates the conjugate transpose. This is a reference clutter signal.

[0056] The method described in the above embodiments accurately identifies the log-normal distribution characteristics of complex clutter such as building static clutter and uses the minimum mean square error (LMS) adaptive filtering algorithm to cancel clutter, thereby achieving efficient suppression of building static clutter and non-zero frequency clutter components with high amplitude and wide spectrum, and improving the accuracy and stability of clutter suppression.

[0057] In some embodiments, after employing a minimum mean square error adaptive filtering method to suppress sensing clutter, the method further includes: Determine the clutter suppression ratio; If the clutter suppression ratio is less than the seventh threshold, the filter coefficients in the minimum mean square error adaptive filtering method are updated, and then clutter suppression is performed until the clutter suppression ratio is greater than the seventh threshold.

[0058] Specifically, after performing sensing clutter suppression, this application calculates the clutter suppression ratio: in, Clutter suppression ratio, The original clutter-containing signal power, To suppress residual clutter signal power, such as Then the clutter suppression achieves the expected effect. For example... Then update the filter coefficients. Repeat the above steps until the clutter suppression ratio meets the condition.

[0059] in, This represents the updated filter coefficients. This represents the filter coefficients before the update. Indicates the step size. It is the conjugate of clutter signals.

[0060] After completing clutter suppression, The signal is essentially a clean target signal. Combined with information from multi-station joint detection, it completes subsequent signal and data processing for position, velocity, etc.

[0061] The method described in the above embodiment quantifies the clutter suppression effect through the clutter suppression ratio. When the suppression effect fails to meet the standard, the filter coefficients of the minimum mean square error adaptive filter are dynamically adjusted, and the clutter suppression process is repeated until the clutter suppression ratio meets the preset conditions, thereby improving the stability and reliability of target detection in complex urban environments.

[0062] The sensing clutter suppression device provided by the present invention will be described below. The sensing clutter suppression device described below can be referred to in correspondence with the sensing clutter suppression method described above. For example, Figure 2 As shown, the sensing clutter suppression device includes: The acquisition module 210 is used to acquire echo signals collected by multiple sensing base stations deployed in spatial diversity. The echo signals include the reflected signals of the sensing target and clutter interference signals. The first determining module 220 is used to determine whether the spatial consistency between the signal points of multiple echo signals is satisfied based on the position information of each signal point in the echo signal collected by each sensing base station. The second determining module 230 is used to determine the sensing target signal point and clutter signal point based on whether the spatial consistency between each signal point is satisfied. The suppression module 240 is used to perform sensing clutter suppression based on clutter signal points.

[0063] Figure 3An example is a schematic diagram of the physical structure of a network-side device, such as... Figure 3 As shown, the network-side device may include a processor 310, a communications interface 320, a memory 330, and a communication bus 340. The processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions in the memory 330 to execute a sensing clutter suppression method. This method includes: acquiring echo signals collected by multiple sensing base stations deployed in spatial diversity, the echo signals containing reflected signals from sensing targets and clutter interference signals; determining whether spatial consistency is satisfied between the signal points of the multiple echo signals based on the location information of each signal point in the echo signals collected by each sensing base station; determining the sensing target signal point and clutter signal point based on whether spatial consistency is satisfied; and performing sensing clutter suppression based on the clutter signal point.

[0064] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0065] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the sensing clutter suppression method provided by the above methods. The method includes: acquiring echo signals collected by multiple sensing base stations deployed in spatial diversity, wherein the echo signals include a sensing target reflection signal and a clutter interference signal; determining whether spatial consistency is satisfied between the signal points of the multiple echo signals based on the position information of each signal point in the echo signals collected by each sensing base station; determining the sensing target signal point and the clutter signal point based on whether spatial consistency is satisfied between the signal points; and performing sensing clutter suppression based on the clutter signal point.

[0066] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the sensing clutter suppression method provided by the above methods. The method includes: acquiring echo signals collected by multiple sensing base stations deployed in spatial diversity, the echo signals containing a sensing target reflection signal and clutter interference signals; determining whether spatial consistency is satisfied between the signal points of the multiple echo signals based on the location information of each signal point in the echo signals collected by each sensing base station; determining the sensing target signal point and clutter signal point based on whether spatial consistency is satisfied between the signal points; and performing sensing clutter suppression based on the clutter signal point.

[0067] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0068] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for suppressing sensing clutter, characterized in that, Sensing base stations used in spatial diversity deployments include: The echo signals collected by multiple sensing base stations deployed in spatial diversity are acquired, and the echo signals include the reflected signals of the sensing target and the clutter interference signals. Based on the location information of each signal point in the echo signal collected by each sensing base station, determine whether the spatial consistency is satisfied among the signal points of multiple echo signals. Based on whether the spatial consistency is satisfied among the various signal points, the target signal points and clutter signal points are determined; Based on the clutter signal points, sensing clutter suppression is performed.

2. The sensing clutter suppression method according to claim 1, characterized in that, Before determining whether spatial consistency is satisfied among the signal points of multiple echo signals based on the location information of each signal point in the echo signals collected by each sensing base station, the method further includes: Spatiotemporal consistency calibration is performed on the echo signals collected by each sensing base station.

3. The sensing clutter suppression method according to claim 1, characterized in that, If the spatial positional deviation between signal points is less than the first threshold and the temporal deviation is less than the second threshold, then the spatial consistency between signal points is determined.

4. The sensing clutter suppression method according to claim 1, characterized in that, The step of determining the target signal point and clutter signal point based on whether the spatial consistency between the various signal points is satisfied includes: Signal points that satisfy spatial consistency as well as motion consistency, polarization consistency and amplitude consistency are identified as sensing target signal points. Signal points that do not meet spatial consistency, or that meet spatial consistency but do not meet the aforementioned motion consistency, polarization consistency, and amplitude consistency, are identified as clutter signal points.

5. The sensing clutter suppression method according to claim 4, characterized in that, If the velocity deviation between signal points is less than the third threshold and the heading deviation is less than the fourth threshold, it is determined that the motion consistency between signal points is satisfied. If the polarization ratio deviation between signal points is less than the fifth threshold, it is determined that the polarization consistency between signal points is satisfied. If the amplitude fluctuation between signal points is less than the sixth threshold, it is determined that the signals meet the amplitude consistency requirement.

6. The sensing clutter suppression method according to any one of claims 1-5, characterized in that, The step of sensing clutter suppression based on the clutter signal points includes: Given that the distribution characteristics of the clutter signal points conform to a normal distribution, a minimum mean square error adaptive filtering method is used to suppress sensing clutter.

7. The sensing clutter suppression method according to claim 6, characterized in that, After employing the minimum mean square error adaptive filtering method to suppress sensing clutter, the method further includes: Determine the clutter suppression ratio; If the clutter suppression ratio is less than the seventh threshold, the filter coefficients in the minimum mean square error adaptive filtering method are updated, and then clutter suppression is performed until the clutter suppression ratio is greater than the seventh threshold.

8. A clutter suppression device, characterized in that, Sensing base stations used in spatial diversity deployments include: The acquisition module is used to acquire echo signals collected by multiple sensing base stations deployed in spatial diversity, wherein the echo signals include the reflected signals of the sensing target and clutter interference signals. The first determining module is used to determine whether the spatial consistency between the signal points of multiple echo signals is satisfied based on the position information of each signal point in the echo signals collected by each sensing base station. The second determining module is used to determine the sensing target signal point and clutter signal point based on whether the spatial consistency between the various signal points is satisfied. The suppression module is used to perform sensing clutter suppression based on the clutter signal points.

9. A network-side 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 sensing clutter suppression method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the sensing clutter suppression method as described in any one of claims 1 to 7.

Images