Communication-aware integrated clutter suppression and parameter estimation method, system, device, and medium

Abstract

The present invention discloses a communication-aware integrated clutter suppression and parameter estimation method, system, device, and medium, relating to the field of wireless communications. The method comprises: estimating carrier frequency deviation and time deviation based on a channel state information matrix; compensating the channel state information matrix based on the carrier frequency deviation and time deviation to obtain a channel state information matrix in a synchronized state; suppressing clutter channel state information on this channel state information matrix to obtain a new channel state information matrix; extracting range-velocity parameter pairs for multiple moving targets from the new channel state information matrix using a parameter estimation algorithm; estimating the direction of incoming waves using a spatial spectrum estimation algorithm to obtain an angle of arrival parameter; and correlating the range-velocity parameter pairs with the angle of arrival parameter to obtain an associated parameter group, thereby enabling wireless sensing of the state of a moving object. The present invention can improve the accuracy of speed and distance estimation of moving objects and reduce computational complexity.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and in particular to a communication-aware integrated clutter suppression and parameter estimation method, system, device and medium. Background Art

[0002] With the growing demand for bandwidth and data rates, wireless communications are moving toward high-frequency bands such as millimeter waves and terahertz, which are suitable for radar sensing applications. Furthermore, high-frequency signals attenuate rapidly during transmission. This results in a further decrease in the spacing between base stations, increasing the probability that terminals and base stations can "see" each other, increasingly satisfying the requirements for wireless sensing applications. Furthermore, the vision of "digital twins" and AI-enabled applications in 6G mobile communications places an urgent need for "environmental perception" capabilities at the physical layer of wireless communication systems. This demand has driven research into integrated communication and perception technologies for 6G.

[0003] Traditional communication and perception technologies use different waveforms, architectures, spectrum resources, and hardware devices, resulting in low resource utilization. Integrated communication and perception technology can integrate perception and communication, allowing them to jointly leverage waveforms, spectrum, antennas, systems, and other hardware and software resources. This results in higher spectrum and device resource utilization, more accurate perception-assisted communication (beam management, beam tracking, etc.), and more sensitive communication-assisted perception (positioning enhancement, gesture recognition, etc.). Furthermore, if integrated communication and perception technology is applied to mobile communication access networks, cellular networks will become ubiquitous RF perception networks, capable of providing real-time target positioning, speed measurement, and environmental imaging.

[0004] Currently, commonly used radar channel estimation methods for integrated communication and sensing technologies (e.g., Chinese patent application publication number CN113363706A) offer high spectral efficiency, low architectural cost, and short processing time. However, they are unable to eliminate the influence of clutter in complex electromagnetic signal propagation environments. This can severely impact the accuracy of velocity and range estimation for moving objects, and can even result in false or missed target detections. Furthermore, commonly used radar channel estimation methods typically perform eigendecomposition on the constructed high-dimensional received signal matrix to correlate parameters, resulting in extremely high computational complexity. Summary of the Invention

[0005] To solve the above problems in the prior art, the present invention provides a communication-aware integrated clutter suppression and parameter estimation method, system, device and medium.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] In a first aspect, the present invention provides a communication-aware integrated clutter suppression and parameter estimation method, the method comprising:

[0008] Obtaining a channel state information matrix;

[0009] Estimating a carrier frequency deviation and a time deviation based on the channel state information matrix;

[0010] Compensating the channel state information matrix based on the carrier frequency deviation and the time deviation to obtain a channel state information matrix in a synchronized state;

[0011] Suppressing the clutter channel state information on the channel state information matrix in the synchronization state to obtain a new channel state information matrix;

[0012] A parameter estimation algorithm is used to extract the range-velocity parameter pairs of multiple moving targets from the new channel state information matrix;

[0013] The spatial spectrum estimation algorithm is used to estimate the direction of the incoming wave and obtain the arrival angle parameters corresponding to multiple moving targets;

[0014] The distance-speed parameter pair and the arrival angle parameter are associated to obtain an associated parameter group, so as to realize the function of wirelessly sensing the state of the moving object.

[0015] Optionally, a moving target detection algorithm is used to suppress clutter channel state information on the channel state information matrix in the synchronization state to obtain a new channel state information matrix.

[0016] Optionally, a parameter estimation algorithm is used to extract the range-velocity parameter pairs of multiple moving targets from the new channel state information matrix, specifically including:

[0017] For multiple OFDM symbols, based on the new channel state information matrix corresponding to each OFDM symbol, a row vector is extracted using a specific criterion, or multiple row vectors are merged into one row vector using a specific merging criterion. The obtained row vectors are stacked in sequence according to the order in which the OFDM symbols are received to obtain a constructed matrix;

[0018] Performing a two-dimensional discrete Fourier transform on the construction matrix to obtain a transformation matrix;

[0019] Arrange the absolute values ​​of all elements of the transformation matrix from large to small, take the row and column index values ​​corresponding to the previous maximum value in turn, realize the corresponding distance-speed parameter estimation, and obtain the corresponding L V The distance-speed parameter pair of a moving target.

[0020] Optionally, associating the distance-speed parameter pair with the arrival angle parameter to obtain an associated parameter group specifically includes:

[0021] constructing a steering matrix according to the arrival angle parameter and establishing an association matrix corresponding to the steering matrix;

[0022] Substituting the distance-speed parameter pair into the correlation matrix for matching and correlation to obtain correlation variables;

[0023] The distance-speed parameter pair and the arrival angle parameter are associated with each other according to the associated variable to obtain the associated parameter group.

[0024] Optionally, substituting the distance-speed parameter pair into the association matrix for matching and association to obtain an association variable specifically includes:

[0025] Substituting the distance-speed parameter pair into the incidence matrix, we get L V matrix elements;

[0026] L V The index corresponding to the element with the largest absolute value among the matrix elements is used as the associated variable.

[0027] Optionally, a channel state information matrix is ​​acquired using a channel estimation method according to a received Orthogonal Frequency-Division Multiplexing (OFDM) signal.

[0028] In a second aspect, the present invention provides a communication-aware integrated clutter suppression and parameter estimation system, which is used to implement the communication-aware integrated clutter suppression and parameter estimation method provided above; the system comprises:

[0029] An information matrix acquisition module is used to obtain a channel state information matrix;

[0030] A deviation determination module, configured to estimate a carrier frequency deviation and a time deviation based on the channel state information matrix;

[0031] An information synchronization module, configured to compensate the channel state information matrix based on the carrier frequency deviation and the time deviation to obtain a channel state information matrix in a synchronized state;

[0032] An information suppression module is used to suppress the clutter channel state information of the channel state information matrix in the synchronization state to obtain a new channel state information matrix;

[0033] A parameter extraction module is used to extract the distance-speed parameter pairs of multiple moving targets from the new channel state information matrix using a parameter estimation algorithm;

[0034] A parameter estimation module is used to estimate the direction of incoming waves using a spatial spectrum estimation algorithm to obtain arrival angle parameters corresponding to multiple moving targets;

[0035] The parameter association module is used to associate the distance-speed parameter pair with the arrival angle parameter to obtain an associated parameter group to achieve the function of wirelessly sensing the state of the moving object.

[0036] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, wherein the memory is used to store a computer program that can be run on the processor, and is characterized in that the processor executes the computer program to implement the steps of the above-mentioned communication perception integrated clutter suppression and parameter estimation method.

[0037] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned communication-aware integrated clutter suppression and parameter estimation method.

[0038] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0039] The communication-aware integrated clutter suppression and parameter estimation method, system, device, and medium provided by the present invention remove clutter components from received signals by processing the channel state information matrix in an asynchronous state and performing a clutter channel state information suppression operation. This reduces the impact of clutter components on speed-range estimation and improves the accuracy of speed and distance estimation for moving objects. Furthermore, by directly correlating range-speed parameter pairs with angle-of-arrival parameters, the present invention eliminates the need for eigendecomposition of the high-dimensional received signal matrix, effectively reducing computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 Flowchart of the communication-aware integrated clutter suppression and parameter estimation method provided in an embodiment of the present invention;

[0042] Figure 2 This is a diagram illustrating an implementation architecture of the communication-aware integrated clutter suppression and parameter estimation method provided in an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of parameter association and matching provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] The purpose of the present invention is to provide a communication-aware integrated clutter suppression and parameter estimation method, system, device and medium, aiming to improve the speed and distance estimation accuracy of moving objects and reduce computational complexity.

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] Example 1

[0048] This embodiment provides a communication-aware integrated clutter suppression and parameter estimation method, such as Figure 1 and Figure 2 As shown, the method includes:

[0049] Step 100: Obtain a channel state information matrix.

[0050] For example, based on the received G OFDM signals, an arbitrary channel estimation method is used to obtain the channel state information matrix set corresponding to these OFDM symbols: Among them, Y g The element in the mth row and nth column is Y g [m,n],Y g [m,n] represents the channel state information of the nth subcarrier corresponding to the mth antenna. Assume that the signal reaches the receiver via L independent propagation paths, and the channel state information corresponding to the nth subcarrier reaching the mth antenna via the lth path is recorded as Y g,l [m,n]. Then, the channel state information Y g [m,n] can be expressed as In L independent propagation paths, there are L signal reflectors [i.e., non-line-of-sight (NLOS) scenario], or L-1 reflectors [i.e., line-of-sight (LOS) scenario]. In actual application, the present invention only cares about reflectors in motion, and assumes that their number is L V .

[0051] Step 101: Estimating the carrier frequency offset and time offset based on the channel state information matrix.

[0052] In actual application, the receiver perception module uses the channel state information matrix set Estimate the carrier frequency offset (CFO) Δf o and time offset (TO) Δτ o The estimation method of CFO and TO is an existing method, for example, refer to the description in the Chinese patent application with publication number CN114079598A.

[0053] Step 102: Compensate the channel state information matrix based on the carrier frequency deviation and the time deviation to obtain the channel state information matrix in the synchronization state. The obtained channel state information matrix set in the synchronization state can be recorded as is the channel state information matrix in the synchronization state. In practical applications, a synchronization scheme can be used to compensate the channel state information matrix set Get the channel state information matrix set in the synchronization state The synchronization scheme can be any one that can obtain Δf o and Δτ o A high-precision synchronization solution, such as the synchronization solution disclosed in Chinese patent application publication number CN114079598A.

[0054] Step 103: Suppress the clutter channel state information of the channel state information matrix in the synchronization state to obtain a new channel state information matrix to improve the subsequent L V Path length And the corresponding reflector's speed The estimation accuracy of . Among them, the new channel state information matrix set is recorded as The clutter channel state information is the channel state information corresponding to the path where the static signal reflector is located. Clutter channel state information suppression can be implemented in the receiver.

[0055] In practical applications, the clutter suppression scheme used can be any clutter suppression scheme, such as, but not limited to, the Moving Target Indication (MTI) algorithm. Since clutter is a signal reflected by a stationary or nearly stationary object, the Doppler shift of this signal component is almost zero. Considering that the gain and delay of the channel are almost unchanged in a short period of time, the channel state information H of the clutter in the gth OFDM symbol is g,clutter It is almost constant with the change of g. In addition, due to pass The clutter channel (i.e., the noise channel) can be eliminated. g,moving is the channel state information corresponding to the path where the moving object is located, For the first gG d OFDM symbols corresponding to the channel state information matrix, where G d An integer parameter is selected.

[0056] Step 104: Use parameter estimation algorithm to extract multiple distance-speed parameter pairs of moving targets from the new channel state information matrix. The distance-speed parameter pair can also be called speed-distance parameter pair, denoted as and are the distance and speed parameters corresponding to the i-th moving target (i.e., moving object).

[0057] The distance of multiple moving targets refers to the distance a wireless signal travels from the transmitter, reflects off an object, and reaches the receiver. The speed of multiple moving targets refers to the projected component of each moving object's speed onto the centerline of the angle formed between the transmitter and the object, and the object and the receiver.

[0058] In actual application, the receiver perception module can be used to estimate the distance and speed parameters, and the corresponding L can be estimated using any parameter estimation algorithm based on the data shared by the communication module. V The distance and / or speed parameters of a moving target.

[0059] The parameter estimation algorithm used is not limited to a specific estimation algorithm. For example, the process of implementing joint distance and speed parameter estimation through two-dimensional discrete Fourier transform (DFT) is as follows:

[0060] For the several matrices obtained by suppressing the clutter channel state information at the receiver perception module Extract each matrix separately The mth row in Get the construction matrix For example, for multiple OFDM symbols, based on the new channel state information matrix corresponding to each OFDM symbol, a row vector is extracted through a specific criterion (such as maximum signal-to-noise ratio), or multiple row vectors are merged into one row vector through a specific merging criterion (such as maximum ratio combining), and the obtained row vectors are stacked in sequence according to the reception order of the OFDM symbols to obtain a constructed matrix.

[0061] By constructing the matrix Γ m Perform a two-dimensional DFT transform to obtain the transformation matrix Then arrange the absolute values ​​of all elements of the transformation matrix from large to small, and take the first L V The row and column index values ​​corresponding to the maximum values ​​are used to estimate the corresponding distance-speed parameters and obtain the corresponding L VThe associated distance-speed parameter pairs of moving targets

[0062] Step 105: Use the spatial spectrum estimation algorithm to estimate the direction of the incoming wave and obtain the arrival angle parameters corresponding to multiple moving targets. V The Direction Of Arrival (DOA) parameter corresponding to a moving target is recorded as The DOA parameter set is recorded as

[0063] In practical applications, the estimation algorithm used in the direction finding process can be any spatial spectrum estimation algorithm, such as the classic Multiple Signal Classification (MUSIC) algorithm or the Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm, but is not limited thereto.

[0064] Taking the MUSIC algorithm as an example, the receiver perception module first calculates the corresponding covariance matrix based on the original received signal, and then performs eigenvalue decomposition on the obtained covariance matrix to obtain the corresponding eigenvalues ​​and eigenvectors. Then, the first L eigenvalues ​​are taken in descending order. V eigenvalues, and construct the corresponding eigenvectors as the signal subspace, and construct the eigenvectors corresponding to the remaining eigenvalues ​​as the noise subspace. Finally, construct the MUSIC spectrum function based on the obtained noise subspace, find the peak position of the MUSIC spectrum function within the scanning angle range, and take the first L V The angle value corresponding to the peak is taken as L V Each moving target corresponds to a DOA parameter.

[0065] Step 106: Associate the distance-speed parameter pair and the arrival angle parameter to obtain an associated parameter group to achieve the function of wirelessly sensing the state of the moving object.

[0066] In actual application, the implementation process of step 106 may be:

[0067] The associated module initialization defines the associated variable as b l ∈{1,…,L V}, as a DOA parameter set The best correlation index between the estimated parameters to be associated with the lth path. Among them, the associated variable provides a correlation matching relationship, and then the associated variable set is realized through the association scheme. Update. Specifically, bl Indicates the speed and / or distance parameters to be associated obtained for the lth path estimation, and the corresponding matching DOA parameters in the DOA parameter set The index value in , thus achieving the final matching parameter group output. Figure 3 As shown, the association module outputs the association variable set For the associated variable b n =m, where n,m∈[1,L V ], indicating the nth parameter to be associated (·) n The corresponding associated variable b n , after passing through the association module, the value is assigned to m, which means that the DOA parameter set matched by the nth parameter to be associated is The mth element in .

[0068] Based on the above description, the association scheme used in the actual application of the present invention can be described as follows: Input distance-speed parameter pair DOA parameter set Associated variables Output associated parameter group Specifically:

[0069] The association module first sets the DOA parameters Building a Guide Collection The guide elements in the guide set are represented as:

[0070]

[0071] in, Indicates that the corresponding DOA parameter at the receiving antenna array is The steering vector at this time, diag{} represents the diagonal matrix function, and repeat G times means that the steering vector is repeated G times. At this time, we have:

[0072]

[0073] Furthermore, the receiver perception module establishes i The corresponding incidence matrix Π i , the incidence matrix Π i Expressed as:

[0074]

[0075] Where, represents the N-dimensional IDFT matrix, (·) H Re(·) represents the real part operation, and F G Represents the G-dimensional inverse discrete Fourier transform matrix.

[0076] For the distance-speed parameter pair to be associated Substitute into the incidence matrix set Perform matching association to obtain the updated associated variable set Among them, the associated variable set The updated associated variable becomes b l ′:

[0077]

[0078] Where, κ l Represents, ∈ l Represents the estimated distance and speed parameters of the lth moving target.

[0079] Among them, the distance-speed parameter to be associated For example, its corresponding associated variable b l The update process is as follows:

[0080] The distance-speed parameter pair Substitute the row and column indices into the incidence matrix set Get L V matrix elements b l Update to this L V The π corresponding to the element with the largest absolute value among the matrix elements i Index, denoted as: b l ′=i.

[0081] The parameter association process here can be interpreted as: the association matrix set The distance-speed parameter pair association, and because π i The DOA parameter Constructed, thus completing the distance-speed parameter pair and DOA parameters association.

[0082] According to the updated associated variable set Set the distance-speed association parameter group and DOA parameter set Then the final correlation parameter group (i.e. speed-distance-DOA correlation parameter group) is output.

[0083] In summary, the present invention provides a solution for asynchronous receivers that combines clutter suppression, transceiver synchronization, and range-velocity-DOA parameter group estimation and matching. The asynchronous receiving device can obtain the DOA parameter set corresponding to each propagation path. Path length and the speed of the signal reflector Based on these three types of parameters, the speed-distance-DOA correlation parameter group was successfully estimated. This enables the function of sensing moving objects.

[0084] Example 2

[0085] This embodiment provides a communication-aware integrated clutter suppression and parameter estimation system for implementing the communication-aware integrated clutter suppression and parameter estimation method provided in Example 1. The system includes an information matrix acquisition module, a deviation determination module, an information synchronization module, an information suppression module, a parameter extraction module, a parameter estimation module, and a parameter association module.

[0086] The information matrix acquisition module is used to obtain the channel state information matrix.

[0087] The deviation determination module is used to estimate the carrier frequency deviation and time deviation based on the channel state information matrix.

[0088] The information synchronization module is used to compensate the channel state information matrix based on the carrier frequency deviation and the time deviation to obtain the channel state information matrix in a synchronized state.

[0089] The information suppression module is used to suppress the clutter channel state information of the channel state information matrix in the synchronization state to obtain a new channel state information matrix.

[0090] The parameter extraction module is used to extract the distance-speed parameter pairs of multiple moving targets from the new channel state information matrix using a parameter estimation algorithm.

[0091] The parameter estimation module is used to estimate the direction of the incoming wave using a spatial spectrum estimation algorithm to obtain arrival angle parameters corresponding to multiple moving targets.

[0092] The parameter association module is used to associate the distance-speed parameter pair and the arrival angle parameter to obtain an associated parameter group to realize the function of wireless sensing of the state of the moving object.

[0093] Example 3

[0094] This embodiment provides a computer device, comprising: a memory and a processor, wherein the memory is used to store a computer program executable on the processor, and the processor executes the computer program to implement the communication-aware integrated clutter suppression and parameter estimation method of embodiment 1.

[0095] Example 4

[0096] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the communication-aware integrated clutter suppression and parameter estimation method in embodiment 1.

[0097] Example 5

[0098] A computer program product includes a computer program, which, when executed by a processor, implements the communication-aware integrated clutter suppression and parameter estimation method in embodiment 1.

[0099] Example 6

[0100] A computer device, which may be a database. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it can implement the communication perception integrated clutter suppression and parameter estimation method in Example 1.

[0101] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0102] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0103] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0104] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above examples is only intended to help understand the method and core concept of the present invention. The same or similar parts between the various examples can be referenced. At the same time, for those skilled in the art, based on the concept of the present invention, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A communication perception integrated clutter suppression and parameter estimation method, characterized in that: The method comprises: Obtaining a channel state information matrix; Estimating a carrier frequency deviation and a time deviation based on the channel state information matrix; Compensating the channel state information matrix based on the carrier frequency deviation and the time deviation to obtain a channel state information matrix in a synchronized state; Suppressing the clutter channel state information on the channel state information matrix in the synchronization state to obtain a new channel state information matrix; A parameter estimation algorithm is used to extract the range-velocity parameter pairs of multiple moving targets from the new channel state information matrix; The spatial spectrum estimation algorithm is used to estimate the direction of the incoming wave and obtain the arrival angle parameters corresponding to multiple moving targets; The distance-speed parameter pair and the arrival angle parameter are associated to obtain an associated parameter group, so as to realize the function of wirelessly sensing the state of the moving object.

2. The communication-aware integrated clutter suppression and parameter estimation method according to claim 1, characterized in that: The moving target detection algorithm is used to suppress the clutter channel state information of the channel state information matrix in the synchronization state to obtain a new channel state information matrix.

3. The communication-aware integrated clutter suppression and parameter estimation method according to claim 1, characterized in that: The parameter estimation algorithm is used to extract the range-velocity parameter pairs of multiple moving targets from the new channel state information matrix, including: For multiple OFDM symbols, based on the new channel state information matrix corresponding to each OFDM symbol, a row vector is extracted using a specific criterion, or multiple row vectors are merged into one row vector using a specific merging criterion. The obtained row vectors are stacked in sequence according to the order in which the OFDM symbols are received to obtain a constructed matrix; Performing a two-dimensional discrete Fourier transform on the construction matrix to obtain a transformation matrix; Arrange the absolute values ​​of all elements of the transformation matrix from large to small, and take the first L V The row and column index values ​​corresponding to the maximum values ​​are used to estimate the corresponding distance-speed parameters and obtain the corresponding L V The distance-speed parameter pair of a moving target.

4. The communication-aware integrated clutter suppression and parameter estimation method according to claim 1, characterized in that: Associating the distance-speed parameter pair with the arrival angle parameter to obtain an associated parameter group, specifically including: constructing a steering matrix according to the arrival angle parameter and establishing an association matrix corresponding to the steering matrix; Substituting the distance-speed parameter pair into the correlation matrix for matching and correlation to obtain correlation variables; The distance-speed parameter pair and the arrival angle parameter are associated with each other according to the associated variable to obtain the associated parameter group.

5. The communication-aware integrated clutter suppression and parameter estimation method according to claim 4, characterized in that: Substitute the distance-speed parameter pair into the association matrix for matching and association to obtain association variables, specifically including: Substituting the distance-speed parameter pair into the incidence matrix, we get L V matrix elements; L V The index corresponding to the element with the largest absolute value among the matrix elements is used as the associated variable.

6. The communication-aware integrated clutter suppression and parameter estimation method according to claim 1, characterized in that: A channel state information matrix is ​​obtained by adopting a channel estimation method according to the received OFDM signal.

7. A communication perception integrated clutter suppression and parameter estimation system, characterized in that: The system is used to implement the communication-aware integrated clutter suppression and parameter estimation method according to any one of claims 1 to 6; the system comprises: An information matrix acquisition module is used to obtain a channel state information matrix; A deviation determination module, configured to estimate a carrier frequency deviation and a time deviation based on the channel state information matrix; An information synchronization module, configured to compensate the channel state information matrix based on the carrier frequency deviation and the time deviation to obtain a channel state information matrix in a synchronized state; An information suppression module is used to suppress the clutter channel state information of the channel state information matrix in the synchronization state to obtain a new channel state information matrix; A parameter extraction module is used to extract the distance-speed parameter pairs of multiple moving targets from the new channel state information matrix using a parameter estimation algorithm; A parameter estimation module is used to estimate the direction of incoming waves using a spatial spectrum estimation algorithm to obtain arrival angle parameters corresponding to multiple moving targets; The parameter association module is used to associate the distance-speed parameter pair with the arrival angle parameter to obtain an associated parameter group to achieve the function of wirelessly sensing the state of the moving object.

8. A computer device comprising: A memory and a processor, wherein the memory is used to store a computer program that can be run on the processor, and wherein the processor executes the computer program to implement the steps of the communication-aware integrated clutter suppression and parameter estimation method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the communication-aware integrated clutter suppression and parameter estimation method according to any one of claims 1 to 6 are implemented.

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