Two-dimensional multiple signal classification sensing method and system based on orthogonal frequency division multiplexing signals

Abstract

The invention discloses a two-dimensional multi-signal classification sensing method and system based on orthogonal frequency division multiplexing signals. At a transmitting end, a signal based on orthogonal frequency division multiplexing and inductance integration is generated and is transmitted to a free space for transmission through a transmitting antenna; and at a receiving end, for echo signals reflected by the target object, obtaining distance and speed information of the target object by adopting a two-dimensional multi-signal classification sensing method. According to the method provided by the invention, the sensing resolution is not influenced by the inherent bandwidth of a communication system by adopting the orthogonal frequency division multiplexing-based sensing integrated signal and utilizing the two-dimensional structure characteristic, the sensing precision can be improved, the dual functions of communication and sensing are realized, the resolution is controllable, and the method can be used for the Internet of Vehicles, smart factories, intelligent traffic and the like.

Description

(I) Technical field

[0001] The present invention relates to the field of communication perception integration, and in particular to a two-dimensional multiple signal classification perception method and system based on orthogonal frequency division multiplexing signals. (II) Background technology

[0002] With the development of emerging businesses such as intelligence, immersion, and digital twins, the demand for information processing such as high-precision detection, positioning, identification, imaging, and large-bandwidth, low-latency information transmission of targets has been greatly enhanced. 6G will provide perception, communication, computing capabilities and data services, ushering in an era of perception, connection, and intelligence of all things. Synaesthesia integration can integrate the two functions of communication and perception, so that future communication systems have both communication and perception functions. While transmitting information through wireless channels, the physical characteristics of the surrounding environment are perceived, so that the communication and perception functions are mutually enhanced. The development of synaesthesia integration adapts to social needs and technological development, and can be used in the fields of Internet of Vehicles, smart factories, smart transportation, smart cities, etc.

[0003] The design of integrated communication and perception waveforms needs to take into account the needs of communication and perception, which can be specifically divided into key requirements common to communication and perception, key requirements mainly for communication, and key requirements mainly for perception. For key requirements mainly for perception, they are mainly reflected in resolution. Resolution refers to the ability to distinguish targets, which can be divided into distance resolution and velocity resolution. Distance resolution is used to characterize the ability of perception to distinguish adjacent targets at distance, usually measured by the minimum resolvable distance; velocity resolution is used to characterize the ability of perception to distinguish targets at radial velocity. In actual system design, due to factors such as subcarriers, fast Fourier transforms, and filter design, there is a gap between different waveforms and parameters and the theoretical upper limit. For multi-dimensional or parameter resolution, the situation is even more complicated. In the current communication and perception integrated system, in terms of target parameter estimation, existing work mainly uses matched filtering technology to obtain target status. However, its performance is limited by inherent resolution, and a wider bandwidth is required to achieve high resolution. (III) Summary of the invention

[0004] In order to solve the above problems, the present invention discloses a two-dimensional multiple signal classification perception method and system based on orthogonal frequency division multiplexing signal. By using an orthogonal frequency division multiplexing synaesthesia integrated signal and utilizing its two-dimensional structural characteristics, the perception resolution is not affected by the inherent bandwidth of the communication system, the perception accuracy can be improved, and the dual functions of communication and perception can be realized at the same time.

[0005] A two-dimensional multiple signal classification perception method based on orthogonal frequency division multiplexing signal, characterized in that the method comprises the following steps:

[0006] At the transmitting end, an orthogonal frequency division multiplexing-based synaesthesia integrated signal is generated and sent to free space for transmission through a transmitting antenna;

[0007] At the receiving end, for the echo signal reflected by the target object, a two-dimensional multiple signal classification sensing method is used to obtain the distance and speed information of the target object; the two-dimensional multiple signal classification sensing method comprises the following steps:

[0008] Step 1: Divide the echo signal by the transmitted OFDM synaesthesia integrated signal to obtain the perception information matrix;

[0009] Step 2: Multiply the perception information matrix by the conjugate transposed matrix of the perception information matrix to obtain the distance covariance matrix; multiply the conjugate transposed matrix of the perception information matrix by the perception information matrix to obtain the speed covariance matrix;

[0010] Step 3: Perform eigendecomposition on the distance covariance matrix and the velocity covariance matrix to obtain the eigenvalues ​​and corresponding eigenvectors of their respective matrices sorted from large to small;

[0011] Step 4: The eigenvector corresponding to the largest eigenvalue among the eigenvalues ​​of the sorted distance and velocity covariance matrices is taken as the signal subspace, and the rest are taken as the noise subspace;

[0012] Step 5: Calculate the distance spectrum function and the velocity spectrum function, and obtain the distance and velocity information of the target object by searching the spectrum peaks.

[0013] Furthermore, in the step 1, the echo signal is divided by the transmitted OFDM synaesthesia integrated signal to obtain a perception information matrix, which is expressed as:

[0014]

[0015] Among them, D r Represents the received echo signal, D t Indicates the transmitted OFDM synaesthesia integrated signal; a r (ω τ ,N c ) represents the delay direction vector, ω τ =2πΔfτ represents the distance radian frequency, Δf represents the subcarrier spacing, τ represents the delay information, N c Indicates the number of subcarriers; a d (ω D ,N s ) represents the Doppler direction vector, ω D =2πT OFDM f D Indicates the speed in radians, T OFDMExpressed as the time including one OFDM symbol and one cyclic prefix, f D represents the Doppler shift, N s represents the number of OFDM symbols in a frame; A(m,n) represents the channel impact, represents additive white Gaussian noise.

[0016] Furthermore, in the step 2, the perception information matrix is ​​multiplied by the conjugate transposed matrix of the perception information matrix to obtain a distance covariance matrix, and the conjugate transposed matrix of the perception information matrix is ​​multiplied by the perception information matrix to obtain a speed covariance matrix. The distance covariance matrix and the speed covariance matrix are respectively expressed as:

[0017]

[0018] Here, H represents the conjugate transpose.

[0019] Furthermore, in the step 3, the distance covariance matrix and the velocity covariance matrix are subjected to eigendecomposition to obtain the eigenvalues ​​and corresponding eigenvectors of their respective matrices sorted from large to small, and the eigenvalues ​​and corresponding eigenvectors of the respective matrices are respectively expressed as:

[0020]

[0021] in, By R r The eigenvectors of By R r , λ represents the eigenvalue, and diag(x) represents the diagonal matrix of the elements of vector x on the diagonal; By R V The characteristic vector of By R V The eigenvalue composition.

[0022] Furthermore, in the step 4, the eigenvector corresponding to the maximum eigenvalue among the eigenvalues ​​after sorting the distance and speed covariance matrix is ​​used as the signal subspace, and the rest are used as the noise subspace. The signal subspace and the noise subspace are respectively expressed as:

[0023] E s =[S1],

[0024]

[0025] E′ s =[S'1],

[0026]

[0027] Among them, E s、E n Denote the signal subspace and noise subspace of the distance respectively, E′ s , E′ n They represent the signal subspace and noise subspace of velocity respectively.

[0028] Furthermore, in step 5, the distance spectrum function and the speed spectrum function are calculated, and the distance and speed information of the target object are obtained by spectrum peak search. The distance spectrum function and the speed spectrum function are respectively expressed as:

[0029]

[0030] in, k r ∈[0,k max_r -1]. k v ∈[0,k max_v -1]. k max_r and k max_v represents the number of virtual snapshots, k r Represents ω τ The corresponding index, k v Represents ω D The target distance and speed information are respectively expressed as:

[0031]

[0032] Where C0 represents the speed of light, f c Indicates the carrier frequency.

[0033] Furthermore, a system for a two-dimensional multiple signal classification perception method based on orthogonal frequency division multiplexing signals is characterized in that it includes: a signal generating module, which is used to generate an orthogonal frequency division multiplexing synaesthesia integrated signal at the transmitting end, and send it to free space for transmission through a transmitting antenna; a signal receiving module, which is used to obtain the distance and speed information of the target object at the receiving end for the echo signal reflected by the target object using a two-dimensional multiple signal classification perception method. (IV) Description of the drawings

[0034] In order to more clearly illustrate the embodiments of the present invention or the prior art solutions, the drawings required for use in the embodiments or the prior art solutions will be briefly introduced below. It is obvious that the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 It is a schematic diagram of a two-dimensional multiple signal classification perception method and system based on orthogonal frequency division multiplexing signals of the present invention;

[0036] Figure 2 A schematic diagram of a two-dimensional multiple signal classification perception method for echo signals according to the present invention. (V) Specific implementation methods

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 work are within the scope of protection of the present invention.

[0038] The technical solutions in the embodiments of the present invention are described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0039] Figure 1 The schematic diagram of the two-dimensional multiple signal classification perception method and system based on orthogonal frequency division multiplexing signal of the present invention. At the transmitting end, the signal generating module generates an integrated signal based on orthogonal frequency division multiplexing, and sends it to free space for transmission through the transmitting antenna. At the receiving end, for the echo signal reflected by the target object, the signal receiving module adopts the two-dimensional multiple signal classification perception method to obtain the distance and speed information of the target object.

[0040] Figure 2 The schematic diagram of the two-dimensional multiple signal classification perception method of the echo signal of the present invention is shown in FIG. In the signal receiving module, the echo signal is divided by the transmitted orthogonal frequency division multiplexing synaesthesia integrated signal to obtain a perception information matrix, which is represented by:

[0041]

[0042] Among them, D r Represents the received echo signal, D t Indicates the transmitted OFDM synaesthesia integrated signal; a r (ω τ ,N c ) represents the delay direction vector, ω τ =2πΔfτ represents the distance radian frequency, Δf represents the subcarrier spacing, τ represents the delay information, N c Indicates the number of subcarriers; a d (ω D ,N s ) represents the Doppler direction vector, ω D =2πT OFDM f D Indicates the speed in radians, T OFDMExpressed as the time including one OFDM symbol and one cyclic prefix, f D represents the Doppler frequency shift, N s represents the number of OFDM symbols in a frame; A(m,n) represents the channel impact, represents additive white Gaussian noise.

[0043] According to the perception information matrix, the distance and speed covariance matrices are obtained, and the distance and speed covariance matrices are respectively expressed as:

[0044]

[0045] Here, H represents the conjugate transpose.

[0046] Then the distance and speed covariance matrices are subjected to eigendecomposition, and the eigenvalues ​​and corresponding eigenvectors of the respective matrices are expressed as follows:

[0047]

[0048] in, By R r The eigenvectors of By R r , λ represents the eigenvalue, and diag(x) represents the diagonal matrix of the elements of vector x on the diagonal; By R V The characteristic vector of By R V The eigenvalue composition.

[0049] The signal subspace and noise subspace are constructed according to the distance and speed covariance matrix, which can be expressed as:

[0050] E s =[S1],

[0051]

[0052] E′ s =[S'1],

[0053]

[0054] Among them, E s 、E n Denote the signal subspace and noise subspace of the distance respectively, E′ s , E′ n They represent the signal subspace and noise subspace of velocity respectively.

[0055] The calculated distance spectrum function and velocity spectrum function are expressed as:

[0056]

[0057] in, k r ∈[0,k max_r -1]. k v ∈[0,k max_v -1]. k max_r and k max_v represents the number of virtual snapshots, k r Represents ω τ The corresponding index, k v Represents ω D The target distance and speed information are respectively expressed as:

[0058]

[0059] Where C0 represents the speed of light, f c Indicates the carrier frequency.

[0060] (VI) Main technical advantages

[0061] The present invention discloses a two-dimensional multiple signal classification perception method and system based on orthogonal frequency division multiplexing signals. At the transmitting end, an orthogonal frequency division multiplexing synaesthesia integrated signal is generated and sent to free space for transmission through an antenna; at the receiving end, a two-dimensional multiple signal classification perception method is used to obtain the distance and speed information of the target object for the echo signal reflected by the target object.

[0062] The method proposed in the present invention adopts an integrated signal based on orthogonal frequency division multiplexing and utilizes its two-dimensional structural characteristics, so that the perception resolution is not affected by the inherent bandwidth of the communication system. It can simultaneously realize the dual functions of communication and perception, and the resolution is controllable. It can be used in the fields of Internet of Vehicles, smart factories, intelligent transportation, smart cities, etc.

Claims

1. A two-dimensional multiple signal classification perception method based on orthogonal frequency division multiplexing signal, characterized in that: The method comprises the following steps: At the transmitting end, an orthogonal frequency division multiplexing synaesthesia integrated signal is generated and sent to free space for transmission through a transmitting antenna; at the receiving end, a two-dimensional multiple signal classification sensing method is used to obtain the distance and speed information of the target object for the echo signal reflected by the target object; the two-dimensional multiple signal classification sensing method comprises the following steps: Step 1: Divide the echo signal by the transmitted OFDM synaesthesia integrated signal to obtain the perception information matrix; Step 2: Multiply the perception information matrix by the conjugate transposed matrix of the perception information matrix to obtain the distance covariance matrix; multiply the conjugate transposed matrix of the perception information matrix by the perception information matrix to obtain the speed covariance matrix; Step 3: Perform eigendecomposition on the distance covariance matrix and the velocity covariance matrix to obtain the eigenvalues ​​and corresponding eigenvectors of their respective matrices sorted from large to small; Step 4: The eigenvector corresponding to the largest eigenvalue among the eigenvalues ​​of the sorted distance and velocity covariance matrices is taken as the signal subspace, and the rest are taken as the noise subspace; Step 5: Calculate the distance spectrum function and the velocity spectrum function, and obtain the distance and velocity information of the target object by searching the spectrum peaks.

2. A two-dimensional multiple signal classification perception method based on orthogonal frequency division multiplexing signal according to claim 1, characterized in that: In the step 1, the echo signal is divided by the transmitted OFDM synaesthesia integrated signal to obtain a perception information matrix, which is expressed as: Among them, D r Represents the received echo signal, D t Indicates the transmitted OFDM synaesthesia integrated signal; a r (ω τ ,N c ) represents the delay direction vector, ω τ =2πΔfτ represents the distance radian frequency, Δf represents the subcarrier spacing, τ represents the delay information, N c Indicates the number of subcarriers; a d (ω D ,N s ) represents the Doppler direction vector, ω D =2πT OFDM f D Indicates the speed in radians, T OFDM Expressed as the time including one OFDM symbol and one cyclic prefix, f D represents the Doppler shift, N s represents the number of OFDM symbols in a frame; A(m,n) represents the channel impact, represents additive white Gaussian noise.

3. The two-dimensional multiple signal classification perception method based on orthogonal frequency division multiplexing signal according to claim 1 is characterized in that: In the step 2, the perception information matrix is ​​multiplied by the conjugate transposed matrix of the perception information matrix to obtain a distance covariance matrix, and the conjugate transposed matrix of the perception information matrix is ​​multiplied by the perception information matrix to obtain a speed covariance matrix. The distance covariance matrix and the speed covariance matrix are respectively expressed as: Here, H represents the conjugate transpose.

4. The two-dimensional multiple signal classification perception method based on orthogonal frequency division multiplexing signal according to claim 1 is characterized in that: In the step 3, the distance covariance matrix and the velocity covariance matrix are subjected to eigendecomposition to obtain the eigenvalues ​​and corresponding eigenvectors of their respective matrices sorted from large to small. The eigenvalues ​​and corresponding eigenvectors of the respective matrices are respectively expressed as: in, By R r The eigenvectors of By R r , λ represents the eigenvalue, and diag(x) represents the diagonal matrix of the elements of vector x on the diagonal line; By R V The characteristic vector of By R V The eigenvalue composition.

5. The two-dimensional multiple signal classification perception method based on orthogonal frequency division multiplexing signal according to claim 1 is characterized in that: In the step 4, the eigenvector corresponding to the maximum eigenvalue among the eigenvalues ​​after sorting the distance and speed covariance matrix is ​​used as the signal subspace, and the rest are used as the noise subspace. The signal subspace and the noise subspace are respectively expressed as: E s =[S1], E′ s =[S'1], Among them, E s 、E n Denote the signal subspace and noise subspace of the distance respectively, E′ s , E′ n They represent the signal subspace and noise subspace of velocity respectively.

6. The two-dimensional multiple signal classification perception method based on orthogonal frequency division multiplexing signal according to claim 1, characterized in that: In the step 5, the distance spectrum function and the speed spectrum function are calculated, and the distance and speed information of the target object are obtained by spectrum peak search. The distance spectrum function and the speed spectrum function are respectively expressed as: in, k max_r and k max_v represents the number of virtual snapshots, k r Represents ω τ The corresponding index, k v Represents ω D The corresponding index. The target distance and speed information are respectively expressed as: Where C0 represents the speed of light, f c Indicates the carrier frequency.

7. A system for a two-dimensional multiple signal classification and perception method based on orthogonal frequency division multiplexing signals, used to implement a two-dimensional multiple signal classification and perception method based on orthogonal frequency division multiplexing signals as claimed in claims 1 to 6, the system comprising: A signal generating module is used to generate an orthogonal frequency division multiplexing synaesthesia integrated signal at the transmitting end, and send it to free space for transmission through a transmitting antenna; The signal receiving module is used to obtain the distance and speed information of the target object by using a two-dimensional multiple signal classification sensing method for the echo signal reflected by the target object at the receiving end.

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