Clutter suppression method and related apparatus

Abstract

Disclosed in embodiments of the present application are a clutter suppression method and a related apparatus. Specifically, the clutter suppression method disclosed in the embodiments of the present application can effectively identify clutter in a received signal, and only suppresses the clutter in the signal, without the need to perform global signal suppression, thereby greatly reducing the computational load. In addition, statistics can be performed on features of the clutter, such as on at least one of spatial variability, aggregability, dimensional correlation, and texture direction of the clutter. Further, a parameter of a filter is adaptively adjusted on the basis of the features of the clutter, that is, a parameter of a filter can be adaptively adjusted on the basis of real-time features of clutter in different scenarios, thereby achieving effective suppression of clutter under various scenarios, and further facilitating improvement of the SCNR and target detection rate and reduction of the false alarm rate in practical applications.

Description

A clutter suppression method and related apparatus

[0001] This application claims priority to Chinese Patent Application No. 202411865326.0, filed on December 17, 2024, entitled "A Clutter Suppression Method and Related Apparatus", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of radar sensing, and more particularly to a clutter suppression method and related apparatus. Background Technology

[0003] Active sensing methods based on radar, base stations, etc. are increasingly used in the security field. For radar sensing methods, various application scenarios face the same problem: the echo of long-distance targets is very weak, and the radar echo contains a lot of clutter interference (such as interference from buildings, ground / trees, ocean waves, etc., both moving / stationary) and noise (equipment hardware noise, phase noise, etc.). The quantitative description of this in the echo is that the signal-to-clutter-noise ratio (SCNR) of the echo is extremely low. The final impact is that the target is blocked by clutter, resulting in a low detection rate and a large number of false alarms. This has led to customers questioning the use of radar sensing methods in current practical applications.

[0004] To address the operational pain point of missed detections and false alarms caused by low SCNR, current radar sensing methods employ a series of clutter suppression techniques, such as clutter map algorithms and Space-Time Adaptive Processing (STAP). These algorithms largely utilize the incoherent characteristics of the target / clutter in different dimensions (time, space, or time) of the original echo to construct filters and suppress clutter, thereby improving SCNR. However, in practical applications, environmental clutter / hardware noise is not static but exhibits significant randomness and variability over time and space. Current technologies lack research on clutter characteristics, and traditional methods typically employ single or fixed parameters to design filters, resulting in unreliable clutter suppression performance in real-world applications. Summary of the Invention

[0005] This application provides a clutter suppression method and related apparatus that can adaptively adjust the filter parameters according to the characteristics of clutter. This is equivalent to being able to adjust the filter parameters in a targeted manner according to the real-time characteristics of clutter in different scenarios, thereby achieving a better clutter suppression effect in various scenarios. This is more conducive to improving SCNR and target detection rate and reducing false alarm rate in practical applications.

[0006] In a first aspect, embodiments of this application provide a clutter suppression method. Specifically, a received first signal is first processed to determine first clutter in the first signal. Then, features of the first clutter are acquired, wherein the features of the first clutter include at least one of the following: spatial variation of the first clutter, aggregation of the first clutter, dimensionality correlation of the first clutter, and texture direction of the first clutter. Furthermore, first parameters of a filter are determined based on the features of the first clutter, and the first clutter is suppressed according to the filter having the first parameters.

[0007] In this implementation, clutter in the received signal can be effectively filtered out, and suppression is performed only on the clutter within the signal, eliminating the need for global signal suppression and significantly reducing computational load. Furthermore, clutter characteristics can be statistically analyzed, for example, by statistically analyzing at least one of the following features: spatial variability, aggregation, dimensionality correlation, and texture orientation. Subsequently, the filter parameters can be adaptively adjusted based on the clutter characteristics, effectively allowing for targeted adjustment of the filter parameters according to the real-time characteristics of clutter in different scenarios. This results in better clutter suppression across various scenarios, which is more beneficial for improving SCNR and target detection rate while reducing false alarm rate in practical applications.

[0008] In some possible implementations, processing the received first signal to determine the first clutter in the first signal includes: processing the first signal to determine the first clutter in at least one processing domain. The processing domain can be of the time domain, Doppler domain, angular domain, or polarization domain; the characteristics of the first clutter include the characteristics of the first clutter in at least one processing domain; and the first parameters of the filter include the first parameters of the filter in at least one processing domain. In other words, the embodiments of this application utilize all possible signal processing domains as much as possible. On the one hand, this allows for a more comprehensive and detailed description of the clutter distribution characteristics, thereby enabling more effective selection of available clutter reference units for clutter suppression. On the other hand, based on the statistically obtained processing domain where clutter actually exists, filters can be flexibly designed and filtered in different processing domains (single domain or multi-dimensional combined processing domains). Both of these aspects enable a more accurate understanding of clutter and targeted and effective clutter suppression, thereby significantly improving SCNR gain.

[0009] In some possible implementations, determining the first parameters of the filter based on the characteristics of the first clutter includes determining the covariance matrix of the filter based on the characteristics of the first clutter. This is equivalent to adaptively adjusting the covariance matrix according to the real-time characteristics of the clutter in different scenarios, calculating the optimal weight vector of the filter using the covariance matrix, and suppressing the clutter, thereby achieving a good suppression effect on clutter in various scenarios.

[0010] In some possible implementations, determining the covariance matrix of the filter based on the characteristics of the first clutter includes: determining the transformation matrix of the filter based on the characteristics of the first clutter; determining the guard unit and reference unit of the filter based on the characteristics of the first clutter; and determining the covariance matrix based on the transformation matrix, guard unit, and reference unit. This is equivalent to flexibly adjusting the transformation matrix, guard unit, and reference unit according to the real-time characteristics of the clutter in different scenarios, so as to adaptively construct the covariance matrix.

[0011] In some possible implementations, determining the filter's transformation matrix based on the characteristics of the first clutter includes: determining the steering vector of the first clutter based on its characteristics; and determining the transformation matrix based on the steering vector of the first clutter. This is equivalent to adaptively constructing steering vectors of different dimensions based on the real-time characteristics of clutter in different scenarios. The dimension of the steering vector determines the dimension of the transformation matrix, which facilitates flexible adjustment of the transformation matrix for clutter with different characteristics.

[0012] In some possible implementations, processing the received first signal to determine a first clutter in the first signal includes: acquiring quantization parameters of the first signal, wherein the quantization parameters include at least one of absolute energy, density, variance, and correlation coefficient; and determining the first clutter in the first signal based on the quantization parameters. A specific implementation for distinguishing between clutter and non-clutter is provided here, enabling rapid and accurate identification of clutter.

[0013] In some possible implementations, the method further includes classifying the first clutter according to quantization parameters, wherein the characteristics of the first clutter include the characteristics of each class of first clutter after classification. By refining the clutter types, it is more beneficial to subsequently fine-tune the filter parameters, resulting in more targeted clutter suppression.

[0014] In some possible implementations, obtaining the characteristics of the first clutter includes determining the characteristics of the first clutter based on quantization parameters. A specific implementation for marking clutter characteristics is provided here, enabling rapid and accurate feature marking of clutter.

[0015] In some possible implementations, after suppressing the first clutter according to a filter with first parameters, the method further includes: acquiring result-level point cloud data and target-level point cloud data of the first signal; determining missed clutter by comparing the result-level point cloud data and the target-level point cloud data, wherein the missed clutter includes unidentified clutter in the first signal and / or unsuppressed clutter in the first clutter; acquiring characteristics of the missed clutter; adjusting the first parameters according to the characteristics of the missed clutter; and suppressing the missed clutter according to a filter with the adjusted first parameters. That is, by identifying the missed clutter and optimizing the filter parameters according to the characteristics of the missed clutter, the clutter suppression effect is further improved.

[0016] In some possible implementations, after suppressing the first clutter according to a filter with first parameters, the method further includes: processing the received second signal to determine a second clutter in the second signal; acquiring the characteristics of the second clutter; if the characteristics of the second clutter meet a consistency condition with the characteristics of the first clutter, then suppressing the second clutter according to the filter with first parameters; if the characteristics of the second clutter do not meet the consistency condition with the characteristics of the first clutter, then determining the second parameters of the filter according to the characteristics of the second clutter, and suppressing the second clutter according to the filter with second parameters. In this implementation, by performing a consistency judgment on the clutter characteristics of consecutive frames, it is determined whether the filter parameters of the previous frame can be reused. If the clutter of consecutive frames meets the consistency condition, using the filter parameters helps reduce computational overhead and improve the timeliness of the algorithm.

[0017] In some possible implementations, the first signal is an echo signal reflected by the target object, which has a good application effect in security fields that use active sensing methods such as radar or base stations.

[0018] Secondly, embodiments of this application provide a clutter suppression device, which includes a receiving module and a processing module. The receiving module is used to: receive a first signal. The processing module is used to: process the first signal to determine a first clutter in the first signal; acquire features of the first clutter, wherein the features of the first clutter include at least one of the spatial variation of the first clutter, the aggregation of the first clutter, the dimensional correlation of the first clutter, and the texture direction of the first clutter; determine a first parameter of a filter based on the features of the first clutter, and suppress the first clutter based on the filter having the first parameter.

[0019] In some possible implementations, the processing module is specifically configured to: process the first signal to determine a first clutter of the first signal in at least one processing domain, wherein the type of the processing domain includes a time domain, a Doppler domain, an angular domain, and a polarization domain, the characteristics of the first clutter include the characteristics of the first clutter in at least one processing domain, and the first parameters of the filter include the first parameters of the filter in at least one processing domain.

[0020] In some possible implementations, the processing module is specifically used to: determine the covariance matrix of the filter based on the characteristics of the first clutter.

[0021] In some possible implementations, the processing module is specifically used to: determine the transformation matrix of the filter based on the characteristics of the first clutter; determine the guard unit and reference unit of the filter based on the characteristics of the first clutter; and determine the covariance matrix based on the transformation matrix, the guard unit, and the reference unit.

[0022] In some possible implementations, the processing module is specifically used to: determine the steering vector of the first clutter based on the characteristics of the first clutter; and determine the transformation matrix based on the steering vector of the first clutter.

[0023] In some possible implementations, the processing module is specifically configured to: acquire quantization parameters of a first signal, wherein the quantization parameters include at least one of absolute energy, density, variance, and correlation coefficient; and determine a first clutter in the first signal based on the quantization parameters.

[0024] In some possible implementations, the processing module is further configured to: classify the first clutter according to quantization parameters, wherein the characteristics of the first clutter include the characteristics of each class of first clutter after classification.

[0025] In some possible implementations, the processing module is specifically used to: determine the characteristics of the first clutter based on quantization parameters.

[0026] In some possible implementations, after the processing module suppresses the first clutter according to a filter having a first parameter, the processing module is further configured to: acquire result-level point cloud data and target-level point cloud data of the first signal; determine missed clutter by comparing the result-level point cloud data and the target-level point cloud data, wherein the missed clutter includes unidentified clutter in the first signal and / or unsuppressed clutter in the first clutter; acquire the characteristics of the missed clutter; adjust the first parameter according to the characteristics of the missed clutter, and suppress the missed clutter according to a filter having the adjusted first parameter.

[0027] In some possible implementations, after the processing module suppresses the first clutter according to the filter with the first parameter, the receiving module is further configured to receive a second signal; the processing module is further configured to: process the second signal to determine the second clutter in the second signal; acquire the characteristics of the second clutter; if the characteristics of the second clutter meet the consistency condition with the characteristics of the first clutter, then suppress the second clutter according to the filter with the first parameter; if the characteristics of the second clutter do not meet the consistency condition with the characteristics of the first clutter, then determine the second parameter of the filter according to the characteristics of the second clutter, and suppress the second clutter according to the filter with the second parameter.

[0028] In some possible implementations, the first signal is the echo signal reflected by the target object.

[0029] Thirdly, embodiments of this application provide a clutter suppression device, which includes instructions that, when executed by the clutter suppression device, cause the clutter suppression device to perform the method described in any embodiment of the first aspect.

[0030] Fourthly, embodiments of this application provide a radar, which includes a transceiver and a clutter suppression device as described in any embodiment of the second aspect or the third aspect. The transceiver is used to transmit and receive signals, and to transmit the received signals to the clutter suppression device.

[0031] Fifthly, embodiments of this application provide a base station, which includes a transceiver and a clutter suppression device as described in any embodiment of the second aspect or the third aspect. The transceiver is used to transmit signals and receive signals, and to transmit the received signals to the clutter suppression device.

[0032] In a sixth aspect, embodiments of this application provide a chip for performing the method as described in any embodiment of the first aspect.

[0033] In this embodiment, clutter in the received signal can be effectively filtered out, suppressing only the clutter within the signal without requiring global signal suppression, thus greatly reducing computational load. Furthermore, clutter characteristics can be statistically analyzed, for example, statistically analyzing at least one of the following features: spatial variability, aggregation, dimensional correlation, and texture orientation. Subsequently, the filter parameters can be adaptively adjusted based on the clutter characteristics, effectively allowing for targeted adjustment of filter parameters according to the real-time characteristics of clutter in different scenarios. This results in better clutter suppression across various scenarios, which is more conducive to improving SCNR and target detection rate while reducing false alarm rate in practical applications. Attached Figure Description

[0034] Figure 1 is a schematic diagram of an application scenario according to an embodiment of this application;

[0035] Figure 2 is a schematic diagram of another application scenario of the present application embodiment;

[0036] Figure 3 is a schematic diagram of one embodiment of the clutter suppression method in this application;

[0037] Figure 4 is a schematic diagram of another implementation of the clutter suppression method in the embodiments of this application;

[0038] Figure 5 is a schematic diagram of another embodiment of the clutter suppression method in this application;

[0039] Figure 6 is a schematic diagram of a clutter suppression device according to an embodiment of this application;

[0040] Figure 7 is a schematic diagram of the structure of a radar according to an embodiment of this application;

[0041] Figure 8 is a schematic diagram of the structure of a base station according to an embodiment of this application. Detailed Implementation

[0042] This application provides a clutter suppression method and related apparatus that can adaptively adjust the filter parameters according to the characteristics of clutter. This is equivalent to being able to adjust the filter parameters in a targeted manner according to the real-time characteristics of clutter in different scenarios, thereby achieving a better suppression effect on clutter in various scenarios. This is more conducive to improving the signal-clutter noise ratio (SCNR) and target detection rate and reducing the false alarm rate in practical applications.

[0043] It should be noted that the terms "first," "second," etc., in this application specification, claims, and the accompanying drawings are used to distinguish similar objects, not to limit a specific order or sequence. It should be understood that the above terms can be interchanged where appropriate so that the embodiments described in this application can be implemented in an order other than that described in this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices. The term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects are in an "or" relationship. It should be understood that in the embodiments of this application, "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information.

[0044] The clutter suppression method and related apparatus provided in this application can be mainly applied to security fields employing active sensing methods, such as radar or base stations. These applications include, but are not limited to, monitoring of unauthorized drones flying in low-altitude environments, aerial navigation of cargo / manned drones, detection of intruding vessels and navigation guidance for vessels entering ports in maritime / water environments, and detection and security monitoring of intruding personnel / vehicles in air traffic control environments. In these scenarios, due to the advantages of active radar sensing methods, such as long-range / wide-area detection, immunity to light interference, and no need for the cooperation of the detected object, active radar sensing has become the main development trend and primary implementation method in the current security / navigation fields. Active radar sensing includes millimeter-wave radar, phased array radar, and integrated sensing methods using base stations.

[0045] To meet the security industry's requirements for high detection rates, low false alarm rates, and high processing timeliness of active sensing methods, clutter suppression technology is essential for active sensing devices (such as radar or integrated sensing devices for base stations). To maximize the SCNR of echo signals, current traditional methods typically employ Space-Time Adaptive Processing (STAP) or clutter maps. Clutter map methods achieve clutter suppression by accumulating the range-Doppler spectral energy of echoes from different channels over multiple frames and removing the clutter map from the original echo of the current frame. However, this method cannot handle suddenly appearing clutter signals, and its clutter suppression capability is limited. Using the STAP method for clutter processing requires constructing a full-space steering vector for the spatial domain (channels) and all Doppler cells, and then performing clutter suppression on a per-range-cell basis. This process consumes significant computing power and time, making it difficult to meet the timeliness requirements of various scenarios. To meet timeliness requirements, traditional algorithms have been further refined with the STAP dimensionality reduction algorithm. However, this method still cannot adapt to the complex and ever-changing clutter environment on-site. To further improve the signal-to-clutter ratio, traditional clutter suppression algorithms have added clutter region identification algorithms. However, current algorithms are based on relatively simple clutter features, and the subsequent clutter suppression algorithms cannot effectively adapt to the clutter data to configure clutter suppression parameters, making them unable to adapt to complex and ever-changing clutter environments in actual processing.

[0046] To improve the SCNR of the raw echo for high detection and low false alarm rate of weak targets by radar, while reducing the computational cost of the algorithm for efficient target detection, this application proposes a clutter suppression method based on clutter feature statistics to address the shortcomings and limitations of traditional calibration methods. This method statistically analyzes the characteristics of clutter in different dimensions, separating clutter from the raw signal. This ensures that the clutter suppression algorithm only processes clutter, avoiding the target detection rate decrease caused by blind processing of the target. Furthermore, based on the analysis results, clutter processing windows with different dimensions and parameters are adaptively constructed, and the required processing units and their corresponding clutter reference unit parameters are flexibly adjusted. This enables the clutter suppression algorithm to flexibly and specifically suppress different types of clutter, ensuring maximum SCNR improvement performance with minimal computational cost. In addition, for clutter that recurs in each frame, the clutter covariance matrix processed above can be automatically used based on the clutter statistics to avoid secondary calculations. At the same time, for clutter that cannot be completely filtered out by single-frame clutter suppression, feature analysis is performed on the residual non-clutter signals after point cloud clustering / tracking and the filtering depth is adjusted.

[0047] Figure 1 is a schematic diagram of an application scenario according to an embodiment of this application. As shown in Figure 1, the base station transmits a signal to the target object and receives the echo signal reflected by the target object to detect and sense the target object. The type of target object depends on the actual application scenario; for example, the target object can be a drone, a ship, or a vehicle, etc., and is not limited here. Specifically, the base station's active antenna unit (AAU) can transmit a signal to the target object and receive the echo signal, and the base station's building base band unit (BBU) can collect the echo signal.

[0048] Figure 2 is a schematic diagram of another application scenario of this application embodiment. As shown in Figure 2, the radar transmits a signal to the target and receives the echo signal reflected by the target to detect and sense the target. The type of target depends on the actual application scenario; for example, the target can be a drone, a ship, or a vehicle, etc., and is not limited here. Specifically, the radar transmitter can transmit a signal to the target and the receiver can receive the echo signal; the echo signal can be collected by the radar's radio frequency processing board.

[0049] The clutter suppression method provided in this application can suppress clutter in echo signals. Specific operations include clutter region identification, clutter feature statistics and feedback, multi-dimensional fusion adaptive filters, and outputting target-level point clouds by combining with a traditional constant false alarm rate (CFAR) algorithm. The clutter suppression method can be deployed on platforms such as edge sensing platforms or central data platforms (including cloud platforms) and run as code or programs. In this application, the device used to execute the clutter suppression method is referred to as a clutter suppression device.

[0050] Taking Figure 1 as an example, in one possible scenario, the clutter suppression device and the base station can be deployed in an integrated manner. For example, a sensing board / smart board can be integrated on the BBU to execute the clutter suppression method and store its intermediate / final result data. In another possible scenario, the clutter suppression device and the base station can also be deployed in a distributed manner. For example, the clutter suppression method and the storage of its intermediate / final result data can be performed in a processing unit or storage unit in a remote cloud.

[0051] Taking Figure 2 as an example, in one possible scenario, the clutter suppression device and radar can be deployed as an integrated unit. For example, the clutter suppression method and the storage of intermediate / final result data can be performed within the radar's processing chip. In another possible scenario, the clutter suppression device and radar can also be deployed in a distributed manner. For example, the clutter suppression method and the storage of intermediate / final result data can be performed in a processing unit or storage unit in a remote cloud environment.

[0052] It should be noted that base stations or radars can also report relevant data, after clutter suppression, to the data management platform. In security and navigation scenarios, multiple sensing modules typically sense targets in multiple areas simultaneously, requiring a central operational control platform to aggregate data from all distributed sensors and further upload it to the data management platform.

[0053] Figure 3 is a schematic diagram of one embodiment of the clutter suppression method in this application. As shown in Figure 3, the clutter suppression method includes the following steps.

[0054] 1. Identify noise in the echo signal.

[0055] In this embodiment, the received echo signal is quantized and characterized to obtain quantization parameters. These quantization parameters include, but are not limited to, at least one of absolute energy, density, variance, and correlation coefficient. Furthermore, clutter in the echo signal is identified based on these quantization parameters. Specifically, based on the aforementioned quantization parameters, a threshold can be set or an adaptive classification algorithm can be used to classify the echo signal. The classification result is at least binary, equivalent to dividing the echo signal into clutter and non-clutter. In some possible implementations, clutter can be further classified based on the quantization parameters to refine the clutter types, which is more beneficial for subsequent fine-tuning of filter parameters and provides stronger targeting for clutter suppression. For example, taking the echo signal density as the quantization parameter, setting one threshold based on density allows the echo signal to be distinguished as clutter or non-clutter based on this threshold; conversely, setting more thresholds based on density allows for further classification of clutter based on these multiple thresholds.

[0056] It should be noted that the embodiments of this application can further process the received echo signal to obtain an echo signal of at least one processing domain, wherein the type of processing domain includes, but is not limited to, time domain, Doppler domain, angular domain, and polarization domain. Accordingly, clutter in the echo signal of each processing domain can be identified according to the above method. If the clutter is further classified, it is equivalent to classifying the clutter in each processing domain.

[0057] This application's embodiments utilize all possible signal processing domains as much as possible. On the one hand, it can more comprehensively and meticulously describe the distribution characteristics of clutter, thereby more effectively selecting usable clutter reference units for clutter suppression. On the other hand, based on the statistically obtained processing domains where clutter actually exists, it allows for flexible filter design and clutter filtering in different processing domains (single domains or multi-dimensional combined processing domains). Both aspects enable a more accurate understanding of clutter and targeted, effective clutter suppression, thereby significantly improving SCNR gain.

[0058] 2. Obtain the characteristics of clutter.

[0059] Specifically, clutter identified in the echo signal is further characterized to obtain its features in different dimensions. These features include, but are not limited to, at least one of the following: spatial variation, aggregation, dimensional correlation, and texture orientation. Spatial variation indicates the difference between adjacent grids in a processing domain; aggregation describes whether the distribution of clutter in a processing domain is concentrated; texture orientation describes the distribution direction or extension direction / length of clutter in the observed processing domain, and this feature can describe whether the clutter exhibits continuous and obvious texture features in the observed processing domain, such as a linear striped distribution.

[0060] It should be understood that if clutter from multiple processing domains is identified, feature labeling is required for the clutter in each processing domain, i.e., acquiring the characteristics of the clutter in each processing domain. Furthermore, if clutter from some processing domains has the same characteristics, the features of the clutter from these processing domains can be merged. Additionally, if the clutter in each processing domain is further classified, the characteristics of each type of clutter in each processing domain need to be acquired.

[0061] In some possible implementations, clutter characteristics can be obtained based on the aforementioned quantization parameters. For example, the spatial variability characteristic can be calculated and evaluated based on the variance and absolute energy values ​​in the quantization parameters. Another example is that if clutter distribution density statistics show that clutter is concentrated in certain processing units within its processing domain, then the clutter's aggregation is considered high. Yet another example is that texture direction can be obtained by synthesizing clutter quantization parameters or directly from image processing methods.

[0062] 3. Determine the filter parameters based on the characteristics of clutter, and suppress clutter using the filter.

[0063] It should be noted that the traditional STAP clutter processing method, after defining a steering vector in a fixed processing domain, further designs a dimension-reduction / global transformation matrix based on the steering vector. It also sets reference and guard cells of fixed size around the unit to be processed according to the defined processing domain, thereby calculating the covariance filtering matrix of the processing unit. Finally, this filtering matrix is ​​applied to the unit to be processed to achieve clutter suppression. However, in actual sensing environments, clutter at different times may be affected by environmental variables / hardware performance, causing it to exhibit unstable or even drastically fluctuating states in different signal processing domains. For example, the movement of unobserved moving targets or unobserved targets that are temporarily stationary but subsequently move in the sensing environment constitutes clutter to the target to be sensed. Clutter changes with environmental variations. Traditional STAP methods typically set fixed parameters when constructing their filters (i.e., filter matrices), such as the selection of the processing domain containing the steering vector and the corresponding number / dimension, the scale of the steering vector (global or selected directions within the processing domain; the steering vector dimension determines the transformation matrix dimension), and the number of reference and guard cells around the cell to be processed (reflecting the number of cells near the processed cell with similar clutter distributions, representing the filter's processing depth). However, this invariant setting of filter parameters is unreasonable for processing dynamic clutter and predictably leads to a deterioration in clutter suppression. Therefore, this application proposes a scheme for adaptively adjusting filter parameters based on clutter characteristics. These filter parameters include multiple aspects such as the filter's dimension, scale, and depth, which will be described in detail below.

[0064] Specifically, a steering vector for the clutter is constructed in at least one processing domain. For example, if the clutter appears at different distances and exhibits different aggregation characteristics at different distances, and the clutter at a fixed distance has a significant linear texture in other domains (such as the Doppler domain) (this linear texture may be distributed throughout the Doppler domain or only in certain Doppler domains), in this case, the time domain can be selected as the processing domain for the filter first. Then, in the time domain, the fast time frequency corresponding to the distance cell where the clutter is located is used, and a steering vector b(f_(b,0)) of different dimensions is adaptively constructed based on its aggregation characteristics. Furthermore, based on its texture distribution characteristics in the Doppler domain, guard cells / reference cells near the processed cell are selected in the Doppler domain, and the number of selected guard cells and reference cells is determined based on the correlation of the textured clutter in the Doppler domain. Finally, the filter construction in the time domain (i.e., the range direction) is completed, and the selected distance cells are filtered. For example, if clutter appears in different directions and its direction changes over time, a spatial steering vector a(θ0) corresponding to the observation angle of the clutter can be selected in the angular domain. This vector can be modified as needed based on the clutter's direction (vertical / horizontal) within each processing cycle; for example, the horizontal direction could be described as a_z(θ_z0), and the vertical direction as a_y(θ_y0). Similarly, the processing methods for the polarization and Doppler domains are similar. The corresponding polarization receiving phase difference / slow time frequency can be selected based on the polarization or Doppler domain to construct steering vectors P_p(η) / d(f_(c,0)) of different dimensions. It should be noted that by combining clutter from all processing domains and considering the clutter's texture direction characteristics, steering vectors from different processing domains can be flexibly combined to construct the covariance matrix. For example, clutter exists in the time domain, the lateral spatial domain, and the longitudinal spatial domain, and the clutter extends along the Doppler dimension. The clutter covariance matrix and filter can be constructed by combining the steering vectors of the clutter in the time domain, the lateral spatial domain, and the longitudinal spatial domain. The filter can then be applied to the Doppler dimension to achieve clutter suppression.

[0065] On the one hand, embodiments of this application can adaptively adjust the transform matrix of the filter according to the characteristics of clutter. The design of the transform matrix mainly involves the selection of the processing domain and the selection of the dimension of the transform matrix. Different processing domains use different dimensions for filtering, and the dimension of the transform matrix determines the region around the clutter unit to be processed that can be used to identify clutter characteristics. Specifically, the local processing region corresponding to the clutter at a given location can be adaptively determined according to the characteristics of clutter in different processing domains (especially the distribution correlation and distribution range of clutter). For example, if the clutter is distributed in the time and spatial domains and extends along the Doppler dimension, a combined time and spatial transform matrix will be adaptively constructed. Furthermore, the size of the local processing region around the clutter location (the clutter unit to be processed) is automatically selected according to the characteristics of the clutter, and the dimension of the transform matrix is ​​adaptively determined to complete the construction of the dimension-reduced transform matrix.

[0066] On the other hand, embodiments of this application can adaptively adjust the training samples according to the characteristics of clutter. It should be understood that the STAP algorithm requires the selection of reference units as training samples. Generally, the training samples must be independently distributed and the number of samples must be at least twice the covariance matrix. Traditional algorithms set fixed reference units for processing, ignoring the relationship between the number of selected reference units and the units to be processed. Embodiments of this application can utilize the texture direction of the clutter to adaptively select the number of training samples in that direction. Specifically, the number of training samples can be adaptively determined based on the clutter distribution parameters around the clutter area to be processed, such as correlation and density parameters, thereby achieving the most accurate clutter estimation with the fewest training samples.

[0067] In one possible implementation, the covariance matrix is ​​estimated by combining the steering vector of at least one processing domain with selected training samples, and the covariance matrix and corresponding parameter data are saved. Then, clutter is suppressed based on a filter having the covariance matrix. For example, the optimal weight vector of the filter is calculated using the covariance matrix, and clutter is suppressed.

[0068] As can be seen from the description of the embodiments shown in Figure 3 above, the solution provided by the embodiments of this application has the following advantages.

[0069] The solution provided in this application can significantly improve the detection rate of weak targets in cluttered environments, while greatly reducing false alarms caused by clutter. By identifying clutter, only clutter is suppressed, avoiding the false weakening of target energy caused by suppressing non-clutter. Based on the feature marking of clutter, a multi-dimensional processing domain can be adaptively selected and a corresponding fusion steering vector can be constructed. Based on the result of feature marking, reliable and stable processing domain and filter parameters are adaptively selected, which can effectively suppress clutter energy and improve the SCNR of weak target echoes, thereby improving the target detection rate and reducing false alarms caused by clutter in the CFAR detection process.

[0070] The solution provided in this application has strong applicability to various scenarios. By characterizing clutter features, the filter parameters can be adaptively adjusted for clutter with different characteristics, thereby flexibly matching clutter with different features and achieving good suppression of clutter in various scenarios, thus improving the scenario applicability of the clutter suppression algorithm.

[0071] The solution provided in this application reduces computational load and improves algorithm timeliness. By effectively filtering clutter and suppressing it only, and considering that clutter occupies only a small portion of the actual processing scenario, the global clutter suppression of traditional algorithms can be avoided, significantly reducing computational load. Furthermore, by characterizing and quantizing clutter features, the filter design process can adaptively select the minimum-dimensional processing domain, the minimum-dimensional transformation matrix, and the minimum number of training units based on the clutter characteristics, greatly reducing the algorithm's computational overhead.

[0072] It should be noted that the embodiment shown in Figure 3 above provides a method for adaptively adjusting filter parameters based on clutter characteristics. Although this can effectively improve the clutter suppression effect, some clutter will inevitably be missed in practical applications. In order to further improve the clutter suppression effect, this application embodiment also provides an implementation method that can effectively suppress missed clutter. The following describes a method for suppressing missed clutter based on the embodiment shown in Figure 3 above.

[0073] Figure 4 is a schematic diagram of another embodiment of the clutter suppression method in this application. As shown in Figure 4, the clutter suppression method includes the following steps.

[0074] 4. Identify missed clutter.

[0075] In this embodiment, CFAR detection is performed on the echo signal after clutter suppression to obtain result-level point cloud data. Clustering and tracking algorithms are then applied to the result-level point cloud data to obtain target-level point cloud data. Furthermore, the result-level and target-level point cloud data are compared. Considering that data that cannot be clustered is usually clutter, the extra data in the result-level point cloud data can be considered as omitted clutter. For example, omitted clutter might be clutter that was not identified in step 1 of the embodiment shown in Figure 3, typically scattered clutter, thus failing to be effectively suppressed. Alternatively, the omitted clutter might be clutter identified in step 1 of the embodiment shown in Figure 3, but which was not effectively suppressed in step 3 of the embodiment shown in Figure 3.

[0076] 5. Obtain the characteristics of missed clutter.

[0077] After identifying the missed clutter, it can be further characterized. It should be understood that step 5 is similar to the implementation of step 2 in the embodiment shown in Figure 3 above; for details, please refer to the relevant description of step 2 in the embodiment shown in Figure 3 above, which will not be repeated here. In addition to the features such as spatial variability, aggregation, dimensionality correlation, and texture direction mentioned above, features such as the correlation, distribution density, and coherence between the missed clutter and adjacent units in different processing domains can also be obtained to further optimize the filter parameters.

[0078] 6. Adjust the filter parameters according to the characteristics of the missed clutter, and suppress the missed clutter through the filter.

[0079] Specifically, for missed clutter, the first step is to determine whether it is located within a clutter region. If not, in subsequent processing frames, it's necessary to determine if the clutter at that location is stable. If stable, the missed clutter is added to the identified clutter regions. If the missed clutter is within a known clutter region, it may exhibit variability compared to clutter in neighboring regions (generally, there might be a jump in amplitude across a certain processing domain). In this case, the filter parameters need to be further adjusted based on the characteristics of the missed clutter. For example, the dimension of the transform matrix and the number of guard cells are adjusted according to the characteristics of the missed clutter. Generally, if the missed clutter exhibits point-like peak clutter, its diffusion degree with adjacent clutter in at least one processing domain needs to be compared. Based on this diffusion degree, the dimension of the transform matrix is ​​increased, the guard cells are matched to the diffusion degree, and the number of reference cells is increased as appropriate. Then, the filtered clutter is suppressed based on the parameter adjustments.

[0080] It should be noted that the embodiment shown in Figure 3 above provides a method for adaptively adjusting filter parameters based on clutter characteristics. Although this effectively improves clutter suppression, it still involves a large number of covariance matrix inversion operations during operation, resulting in a significant computational load per frame. Therefore, this application embodiment also provides a method for determining the consistency of clutter features between consecutive frames to determine whether the filter parameters of the previous frame can be reused, which helps reduce computational overhead. The method for determining the consistency of clutter features between consecutive frames is further described below based on the embodiment shown in Figure 3.

[0081] Figure 5 is a schematic diagram of another embodiment of the clutter suppression method in this application. As shown in Figure 5, the clutter suppression method includes the following steps.

[0082] 7. Perform consistency judgment on the characteristics of clutter identified at different times.

[0083] It should be noted that, considering that receiving the echo signal and suppressing clutter in the echo signal is a continuous process, if the clutter in the echo signals received at different times has the same or similar characteristics, and the corresponding filter parameters have been determined based on the clutter identified at earlier times, then the determined filter parameters can be reused to suppress clutter identified at later times, effectively reducing computational overhead. Specifically, based on the period of the transmitted signal, the echo signal can be divided into different frames. The embodiment shown in Figure 3 above describes the process of suppressing clutter in the echo signal of the first frame. Then, the echo signal of the second frame is received, and the clutter in the echo signal of the second frame is identified, in a manner similar to step 1 in the embodiment shown in Figure 3 above, and will not be repeated here. Next, the characteristics of the clutter in the echo signal of the second frame are obtained, in a manner similar to step 2 in the embodiment shown in Figure 3 above, and will not be repeated here. Furthermore, it is determined whether the clutter characteristics in the echo signal of the second frame meet the consistency criteria with those in the echo signal of the first frame. Specifically, this can be determined by analyzing the correlation between the clutter characteristics in the echo signal of the second frame and those in the echo signal of the first frame. It should be understood that the consistency criteria set in this application are intended to distinguish whether clutter at different times has a high degree of correlation or similarity. For example, it can be achieved by comparing a certain quantization feature of the two; if the difference is less than a preset value, it can be considered to meet the consistency criteria. In addition, any consistency criteria that can effectively distinguish whether there is a high degree of correlation or similarity are applicable, and no specific limitation is made here.

[0084] In one possible scenario, if the clutter characteristics in the echo signal of the second frame do not meet the consistency condition with those in the echo signal of the first frame, indicating a significant difference in clutter characteristics, the filter parameters are re-determined based on the clutter characteristics in the echo signal of the second frame, and the clutter in the echo signal of the second frame is suppressed by the filter. This is equivalent to re-executing step 3 in the embodiment shown in Figure 3 above. For specific implementation details, please refer to the relevant description of step 3 in the embodiment shown in Figure 3 above, which will not be repeated here.

[0085] 8. Reuse the parameters of the already determined filter.

[0086] In another possible scenario, if the clutter characteristics in the echo signal of the second frame meet the consistency condition with those in the echo signal of the first frame, indicating that the clutter characteristics are the same or similar to those in the echo signal of the first frame, then step 8 can be executed, i.e., the parameters of the already determined filter can be reused. In other words, it is not necessary to adjust the filter parameters determined based on the clutter characteristics in the echo signal of the first frame. Reusing the filter parameters helps to reduce computational overhead and improve the timeliness of the algorithm.

[0087] This application also provides apparatus and equipment for implementing the above-described clutter suppression method, which are described below.

[0088] Figure 6 is a schematic diagram of a clutter suppression device according to an embodiment of this application. As shown in Figure 6, the clutter suppression device includes a receiving module 10 and a processing module 20. Specifically, the receiving module 10 is used to perform the operation of receiving echo signals, and the processing module 20 is used to perform the various steps in the embodiments shown in Figures 3 to 5 above.

[0089] It should be understood that the clutter suppression device shown in Figure 6 can also be implemented in other ways. For example, the unit division in the above device is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system. In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or they can be independent physical units, or two or more functional units can be integrated into one processing unit. The integrated units described above can be implemented in hardware or in the form of software functional units.

[0090] Figure 7 is a schematic diagram of a radar structure according to an embodiment of this application. As shown in Figure 7, the radar includes a transceiver unit and a clutter suppression unit. Specifically, the transceiver unit is used to transmit signals to a target and receive the echo signals reflected by the target, and then transmit the echo signals to the clutter suppression unit. The clutter suppression unit is used to perform the above-described clutter suppression method. The structure of the clutter suppression unit can be as shown in Figure 6, and will not be described in detail here.

[0091] Figure 8 is a schematic diagram of a base station structure according to an embodiment of this application. As shown in Figure 8, the base station includes a transceiver unit and a clutter suppression unit. Specifically, the transceiver unit is used to transmit signals to a target object and receive the echo signals reflected by the target object, and then transmit the echo signals to the clutter suppression unit. The clutter suppression unit is used to perform the above-described clutter suppression method. The structure of the clutter suppression unit can be as shown in Figure 6, and will not be described in detail here.

[0092] This application also provides a chip. The chip integrates circuitry for implementing the functions of the processing module 20 described above, and one or more interfaces. As an example, the chip integrates a memory. As another example, when the chip does not integrate a memory, it can be connected to an external memory via the interface. The chip can perform the method steps of any one or more of the foregoing embodiments. Alternatively, the chip can implement the actions performed by the processing and transmission device in the foregoing embodiments based on program code stored in the memory.

[0093] As an example, the chip in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor, any conventional processor, or a processing circuit that implements a specific function.

[0094] This application also provides a computer-readable storage medium including a program or instructions that, when run on a computer, cause the method performed as described in the above method embodiments to be implemented.

[0095] It should be understood that the processing module 20 mentioned in the embodiments of this application can be implemented in hardware or software. When implemented in hardware, the processing module 20 can be a logic circuit, integrated circuit, etc. When implemented in software, the processing module 20 can be a general-purpose processor that reads software code stored in memory. The memory can be independent and connected to the processor, or the memory can be integrated with the processor.

[0096] As an example, the processing module 20 in the embodiments of this application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, any conventional processor, or a processing circuit that implements a specific function.

[0097] In embodiments of this application, the memory may be random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and storage medium may reside in an ASIC. Additionally, the ASIC may reside in a network device or a terminal device. Alternatively, the processor and storage medium may exist as discrete components in the network device or terminal device.

[0098] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof.

[0099] When implemented in hardware, the methods provided in this application embodiment may be implemented without reading software code or instructions. For example, they may be implemented using a CPU, DSP, ASIC, FPGA, other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0100] When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, all or part of the processes or functions of the embodiments of this application are performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a terminal device, or other programmable device. The computer program or instructions can be stored in or transmitted through a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a Digital Versatile Disc (DVD); or it can be a semiconductor medium, such as a solid-state disk (SSD).

[0101] Finally, it should be noted that the above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included 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 spurious signal suppression method, characterized by, include: The received first signal is processed to determine the first clutter in the first signal; The features of the first clutter are obtained, wherein the features of the first clutter include at least one of the following: the spatial variation of the first clutter, the aggregation of the first clutter, the dimensionality correlation of the first clutter, and the texture direction of the first clutter. The first parameters of the filter are determined based on the characteristics of the first clutter, and the first clutter is suppressed based on the filter having the first parameters.

2. The method of claim 1, wherein, Processing the received first signal to determine the first clutter in the first signal includes: The first signal is processed to determine a first clutter of the first signal in at least one processing domain, wherein the type of the processing domain includes time domain, Doppler domain, angular domain and polarization domain, the characteristics of the first clutter include the characteristics of the first clutter in the at least one processing domain, and the first parameters of the filter include the first parameters of the filter in the at least one processing domain.

3. The method according to claim 1 or 2, characterized in that, Determining the first parameters of the filter based on the characteristics of the first clutter includes: The covariance matrix of the filter is determined based on the characteristics of the first clutter.

4. The method of claim 3, wherein, Determining the covariance matrix of the filter based on the characteristics of the first clutter includes: The transformation matrix of the filter is determined based on the characteristics of the first clutter; The protection unit and reference unit of the filter are determined based on the characteristics of the first clutter; The covariance matrix is ​​determined based on the transformation matrix, the protection unit, and the reference unit.

5. The method of claim 4, wherein, Determining the transform matrix of the filter based on the characteristics of the first clutter includes: The steering vector of the first clutter is determined based on its characteristics; The transformation matrix is ​​determined based on the steering vector of the first clutter.

6. The method according to any one of claims 1 to 5, characterized in that, Processing the received first signal to determine the first clutter in the first signal includes: The quantization parameters of the first signal are obtained, wherein the quantization parameters include at least one of absolute energy, density, variance and correlation coefficient; The first clutter in the first signal is determined based on the quantization parameters.

7. The method of claim 6, wherein, The method further includes: The first clutter is classified according to the quantization parameters, wherein the characteristics of the first clutter include the characteristics of each class of first clutter after classification.

8. The method according to claim 6 or 7, characterized in that, The characteristics of the first clutter include: The characteristics of the first clutter are determined based on the quantization parameters.

9. The method according to any one of claims 1 to 8, characterized in that, After suppressing the first clutter using a filter with the first parameter, the method further includes: Acquire the result-level point cloud data and target-level point cloud data of the first signal; The missing clutter is determined by comparing the result-level point cloud data and the target-level point cloud data, wherein the missing clutter includes clutter that was not identified in the first signal and / or clutter that was not suppressed in the first clutter. To obtain the characteristics of the missed clutter; The first parameter is adjusted according to the characteristics of the omitted clutter, and the omitted clutter is suppressed according to the filter with the adjusted first parameter.

10. The method according to any one of claims 1 to 9, characterized in that, After suppressing the first clutter using a filter with the first parameter, the method further includes: The received second signal is processed to determine the second clutter in the second signal; Obtain the characteristics of the second clutter; If the characteristics of the second clutter meet the consistency condition with the characteristics of the first clutter, then the second clutter is suppressed according to the filter with the first parameter; If the characteristics of the second clutter do not conform to the consistency condition with the characteristics of the first clutter, then the second parameter of the filter is determined according to the characteristics of the second clutter, and the second clutter is suppressed according to the filter having the second parameter.

11. The method according to any one of claims 1 to 10, characterized in that, The first signal is the echo signal reflected by the target object.

12. A spurious signal suppressing device characterized by comprising: include: Receive module and processing module; The receiving module is used to: receive a first signal; The processing module is used to: process the first signal to determine a first clutter in the first signal; The features of the first clutter are obtained, wherein the features of the first clutter include at least one of the following: the spatial variation of the first clutter, the aggregation of the first clutter, the dimensionality correlation of the first clutter, and the texture direction of the first clutter. The first parameters of the filter are determined based on the characteristics of the first clutter, and the first clutter is suppressed based on the filter having the first parameters.

13. The spurious signal suppression device of claim 12, wherein, The processing module is specifically used for: The first signal is processed to determine a first clutter of the first signal in at least one processing domain, wherein the type of the processing domain includes time domain, Doppler domain, angular domain and polarization domain, the characteristics of the first clutter include the characteristics of the first clutter in the at least one processing domain, and the first parameters of the filter include the first parameters of the filter in the at least one processing domain.

14. The spurious signal suppression device of claim 12 or 13, wherein The processing module is specifically used for: The covariance matrix of the filter is determined based on the characteristics of the first clutter.

15. The spurious signal suppression device of claim 14, wherein, The processing module is specifically used for: The transformation matrix of the filter is determined based on the characteristics of the first clutter; The protection unit and reference unit of the filter are determined based on the characteristics of the first clutter; The covariance matrix is ​​determined based on the transformation matrix, the protection unit, and the reference unit.

16. The spurious signal suppression device of claim 15, wherein, The processing module is specifically used for: The steering vector of the first clutter is determined based on its characteristics; The transformation matrix is ​​determined based on the steering vector of the first clutter.

17. The spurious signal suppression device of any one of claims 12 to 16, wherein, The processing module is specifically used for: The quantization parameters of the first signal are obtained, wherein the quantization parameters include at least one of absolute energy, density, variance and correlation coefficient; The first clutter in the first signal is determined based on the quantization parameters.

18. The spurious signal suppression device of claim 17, wherein, The processing module is also used for: The first clutter is classified according to the quantization parameters, wherein the characteristics of the first clutter include the characteristics of each class of first clutter after classification.

19. The spurious signal suppression device of claim 17 or 18, wherein, The processing module is specifically used for: The characteristics of the first clutter are determined based on the quantization parameters.

20. The spurious signal suppression device of any one of claims 12 to 19, wherein, After the processing module suppresses the first clutter according to the filter having the first parameter, the processing module is further configured to: Acquire the result-level point cloud data and target-level point cloud data of the first signal; The missing clutter is determined by comparing the result-level point cloud data and the target-level point cloud data, wherein the missing clutter includes clutter that was not identified in the first signal and / or clutter that was not suppressed in the first clutter. To obtain the characteristics of the missed clutter; The first parameter is adjusted according to the characteristics of the omitted clutter, and the omitted clutter is suppressed according to the filter with the adjusted first parameter.

21. The spurious signal suppression device of any one of claims 12 to 20, wherein, After the processing module suppresses the first clutter according to the filter with the first parameter, the receiving module is also used to receive the second signal; The processing module is further configured to: process the second signal to determine a second clutter in the second signal; Obtain the characteristics of the second clutter; If the characteristics of the second clutter meet the consistency condition with the characteristics of the first clutter, then the second clutter is suppressed according to the filter with the first parameter; If the characteristics of the second clutter do not conform to the consistency condition with the characteristics of the first clutter, then the second parameter of the filter is determined according to the characteristics of the second clutter, and the second clutter is suppressed according to the filter having the second parameter.

22. The spurious signal suppression device of any one of claims 12 to 21, wherein, The first signal is the echo signal reflected by the target object.

23. A spurious emission suppressing device, characterized by comprising: The clutter suppression device includes instructions that, when executed by the clutter suppression device, cause the clutter suppression device to perform the method as described in any one of claims 1 to 11.

24. A radar, characterized by Includes a transceiver and a clutter suppression device as described in any one of claims 12 to 23; The transceiver is used to transmit and receive signals, and to transmit the received signals to the clutter suppression device.

25. A base station, comprising: Includes a transceiver and a clutter suppression device as described in any one of claims 12 to 23; The transceiver is used to transmit and receive signals, and to transmit the received signals to the clutter suppression device.

26. A chip, characterized by The chip is used to perform the method as described in any one of claims 1 to 11.