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

Abstract

The application discloses a two-dimensional multiple signal classification sensing method and system based on an orthogonal frequency division multiplexing signal. At a transmitting end, an integrated signal based on an orthogonal frequency division multiplexing signal is generated and transmitted to free space through a transmitting antenna; at a receiving end, for echo signals reflected by a target object, a two-dimensional multiple signal classification sensing method is used to obtain distance and speed information of the target object. The method provided by the application uses an integrated signal based on an orthogonal frequency division multiplexing signal, utilizes the two-dimensional structure characteristics of the integrated signal, makes the sensing resolution not affected by the inherent bandwidth of a communication system, improves sensing accuracy, realizes the functions of communication and sensing at the same time, and the resolution is controllable, and the method can be used in vehicle networking, intelligent factories, intelligent transportation and the like.

Description

(I) Technical Field

[0001] This invention relates to the field of integrated communication and sensing, specifically to a two-dimensional multi-signal classification and sensing method and system based on orthogonal frequency division multiplexing signals. (II) Background Technology

[0002] With the development of emerging businesses such as intelligentization, immersive experiences, and digital twins, the demand for high-precision target detection, positioning, identification, imaging, and high-bandwidth, low-latency information transmission has been greatly enhanced. 6G will provide sensing, communication, and computing capabilities and data services, ushering in an era of ubiquitous sensing, connectivity, and intelligence. Sensor-communication integration can merge communication and sensing functions, enabling future communication systems to simultaneously possess both communication and sensing capabilities. While transmitting information via wireless channels, it senses the physical characteristics of the surrounding environment, thus mutually reinforcing communication and sensing functions. The development of sensor-communication integration adapts to social needs and technological advancements and can be applied to fields such as connected vehicles, smart factories, intelligent transportation, and smart cities.

[0003] The waveform design for integrated communication and sensing needs to consider both communication and sensing requirements, which can be specifically divided into key requirements shared by both communication and sensing, key requirements primarily focused on communication, and key requirements primarily focused on sensing. For key requirements primarily focused on sensing, the main focus is on resolution. Resolution refers to the ability to distinguish targets, and can be divided into range resolution and velocity resolution. Range resolution characterizes the ability of sensing to distinguish nearby targets by distance, usually measured by the minimum resolvable distance; velocity resolution characterizes the ability of sensing to distinguish targets by radial velocity. In practical system design, due to factors such as subcarriers, Fast Fourier Transform, and filter design, different waveforms and parameters deviate from the theoretical upper limit. For multi-dimensional or parameter-based resolution, the situation is even more complex. Current integrated communication and sensing systems mainly use matched filtering techniques to obtain target states for target parameter estimation. However, its performance is limited by inherent resolution, requiring a wider bandwidth to achieve high resolution. (III) Summary of the Invention

[0004] To address the aforementioned problems, this invention discloses a two-dimensional multi-signal classification and sensing method and system based on orthogonal frequency division multiplexing (OFDM) signals. By employing an integrated OFDM sensing signal, leveraging its two-dimensional structural characteristics, the sensing resolution is unaffected by the inherent bandwidth of the communication system, thus improving sensing accuracy and simultaneously achieving both communication and sensing functions.

[0005] A two-dimensional multi-signal classification and sensing method based on orthogonal frequency division multiplexing (OFDM) signals, characterized in that the method includes the following steps:

[0006] At the transmitting end, an integrated signal based on orthogonal frequency division multiplexing is generated and transmitted to free space via the transmitting antenna;

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

[0008] Step 1: Divide the echo signal by the transmitted orthogonal frequency division multiplexing integrated sensing signal to obtain the sensing information matrix;

[0009] Step 2: Multiply the perception information matrix by its conjugate transpose to obtain the distance covariance matrix; multiply the perception information matrix by its conjugate transpose to obtain the velocity covariance matrix.

[0010] Step 3: Perform eigenvalue decomposition on the distance covariance matrix and the velocity covariance matrix to obtain the eigenvalues ​​and corresponding eigenvectors of each matrix in descending order;

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

[0012] Step 5: Calculate the distance spectrum function and velocity spectrum function, and obtain the distance and velocity information of the target object through spectral peak search.

[0013] Furthermore, in step one, the echo signal is divided by the transmitted orthogonal frequency division multiplexing integrated sensing signal to obtain the sensing information matrix, which is represented as follows:

[0014]

[0015] Among them, D r D represents the received echo signal. t This indicates the transmitted orthogonal frequency division multiplexing integrated sensing signal; a r (ω τ N c ) represents the time delay direction vector, ω τ =2πΔfτ represents the distance-in-radians frequency, Δf represents the subcarrier spacing, τ represents the time delay information, and N c Indicates the number of subcarriers; a d (ω D N s ) represents the Doppler direction vector, ω D =2πT OFDM f D T represents the frequency in radians of velocity. OFDMThe time, f, is represented by an OFDM symbol and a cyclic prefix. D N represents the Doppler frequency shift. s This represents the number of OFDM symbols in a frame; A(m,n) represents the channel effect. This represents additive white Gaussian noise.

[0016] Furthermore, in step two, the perception information matrix is ​​multiplied by its conjugate transpose to obtain the distance covariance matrix, and the conjugate transpose of the perception information matrix is ​​multiplied by the perception information matrix to obtain the velocity covariance matrix. The distance covariance matrix and the velocity covariance matrix are respectively expressed as follows:

[0017]

[0018] Where H represents the conjugate transpose.

[0019] Furthermore, in step three, eigenvalue decomposition is performed on the distance covariance matrix and the velocity covariance matrix to obtain their respective eigenvalues ​​and corresponding eigenvectors sorted from largest to smallest. The eigenvalues ​​and corresponding eigenvectors of each matrix are expressed as follows:

[0020]

[0021] in, By R r The eigenvectors are composed of, Represents R r eigenvectors, By R r The eigenvalues ​​are composed of, Represents R r The eigenvalues ​​of vector x are represented by diag(x), which is a diagonal matrix containing the elements of vector x. By R V The eigenvectors are composed of, By R V It consists of eigenvalues.

[0022] Furthermore, in step four, the eigenvector corresponding to the largest eigenvalue among the sorted eigenvalues ​​of the distance and velocity covariance matrices is taken as the signal subspace, and the rest are taken as the noise subspace. The signal subspace and noise subspace are respectively represented as follows:

[0023] E s =[S1]

[0024]

[0025] E s ′ =[S ′ 1]

[0026]

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

[0028] Furthermore, in step five, the distance spectrum function and velocity spectrum function are calculated, and the distance and velocity information of the target object are obtained through spectral peak search. The distance spectrum function and velocity spectrum function are respectively expressed as:

[0029]

[0030] in, k max_r and k max_v k represents the number of virtual snapshots for distance and speed, respectively. r Represents ω τ The corresponding index, k v Represents ω D The corresponding index; the distance and velocity information of the target object are respectively represented as:

[0031]

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

[0033] Furthermore, a system for a two-dimensional multi-signal classification and sensing method based on orthogonal frequency division multiplexing (OFDM) signals is characterized by comprising: a signal generation module, used at the transmitting end to generate an integrated OFDM-based sensing signal and transmit it to free space via a transmitting antenna; and a signal receiving module, used at the receiving end to obtain the distance and velocity information of the target object by employing the two-dimensional multi-signal classification and sensing method on the echo signal reflected by the target object. (iv) Description of the attached drawings

[0034] To more clearly illustrate the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic diagram of the two-dimensional multi-signal classification and sensing method and system based on orthogonal frequency division multiplexing signals according to the present invention;

[0036] Figure 2 This is a schematic diagram of the two-dimensional multi-signal classification and sensing method for echo signals according to the present invention. (V) Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0038] The technical solutions of the present invention will be described in detail below with reference to the accompanying drawings in the embodiments of the present invention.

[0039] Figure 1 This is a schematic diagram of the two-dimensional multi-signal classification and sensing method and system based on orthogonal frequency division multiplexing (OFDM) signals according to the present invention. At the transmitting end, the signal generation module generates an integrated OFDM-based sensing signal, which is transmitted to free space via a transmitting antenna. At the receiving end, for the echo signal reflected by the target object, the signal receiving module uses the two-dimensional multi-signal classification and sensing method to obtain the distance and velocity information of the target object.

[0040] Figure 2 This is a schematic diagram of the two-dimensional multi-signal classification sensing method for echo signals according to the present invention. In the signal receiving module, the echo signal and the transmitted orthogonal frequency division multiplexing integrated sensing signal are divided by a dot matrix to obtain the sensing information matrix, which is represented as follows:

[0041]

[0042] Among them, D r D represents the received echo signal. t This indicates the transmitted orthogonal frequency division multiplexing integrated sensing signal; a r (ω τ N c ) represents the time delay direction vector, ω τ =2πΔfτ represents the distance-in-radians frequency, Δf represents the subcarrier spacing, τ represents the time delay information, and N c Indicates the number of subcarriers; a d (ω D N s ) represents the Doppler direction vector, ω D =2πT OFDMf D T represents the frequency in radians of velocity. OFDM The time, f, is represented by an OFDM symbol and a cyclic prefix. D N represents the Doppler frequency shift. s This represents the number of OFDM symbols in a frame; A(m,n) represents the channel effect. This represents additive white Gaussian noise.

[0043] Based on the perception information matrix, the distance and velocity covariance matrices are obtained, and the distance and velocity covariance matrices are expressed as follows:

[0044]

[0045] Where H represents the conjugate transpose.

[0046] Next, eigenvalue decomposition is performed on the distance and velocity covariance matrices. The eigenvalues ​​and corresponding eigenvectors of each matrix are expressed as follows:

[0047]

[0048] in, By R r The eigenvectors are composed of, Represents R r eigenvectors, By R r The eigenvalues ​​are composed of, Represents R r The eigenvalues ​​of vector x are represented by diag(x), which is a diagonal matrix containing the elements of vector x. By R V The eigenvectors are composed of, Represents R V eigenvectors, By R V The eigenvalues ​​are composed of, Represents R V eigenvalues.

[0049] The signal subspace and noise subspace are constructed based on the distance and velocity covariance matrices, and are represented as follows:

[0050] E s =[S1]

[0051]

[0052] E s ′ =[S ′ 1]

[0053]

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

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

[0056]

[0057] in, k max_r and k max_v k represents the number of virtual snapshots for distance and speed, respectively. r Represents ω τ The corresponding index, k v Represents ω D The corresponding index; the distance and velocity information of the target object are respectively represented as:

[0058]

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

[0060] (vi) Key Technological Advantages

[0061] This invention discloses a two-dimensional multi-signal classification and sensing method and system based on orthogonal frequency division multiplexing (OFDM) signals. At the transmitting end, an OFDM-based integrated sensing signal is generated and transmitted to free space via an antenna. At the receiving end, the distance and velocity information of the target object are obtained using the two-dimensional multi-signal classification and sensing method for the echo signal reflected from the target object.

[0062] The method proposed in this invention uses an integrated sensing signal based on orthogonal frequency division multiplexing, which utilizes its two-dimensional structural characteristics to make the sensing resolution unaffected by the inherent bandwidth of the communication system. It can simultaneously realize dual functions of communication and sensing, and the resolution is controllable. It can be used in fields such as vehicle networking, smart factories, intelligent transportation, and smart cities.

Claims

1. A two-dimensional multi-signal classification and sensing method based on orthogonal frequency division multiplexing (OFDM) signals, characterized in that, The method includes the following steps: At the transmitting end, an integrated orthogonal frequency division multiplexing (OFDM) sensing signal is generated and transmitted to free space via a transmitting antenna. At the receiving end, for the echo signal reflected by the target object, a two-dimensional multi-signal classification sensing method is used to obtain the distance and velocity information of the target object. The two-dimensional multi-signal classification sensing method includes the following steps: Step 1: Divide the echo signal by the transmitted orthogonal frequency division multiplexing integrated sensing signal to obtain the sensing information matrix; the sensing information matrix is ​​represented as: Among them, D r D represents the received echo signal. t This indicates the transmitted orthogonal frequency division multiplexing integrated sensing signal; a r (ω τ N c ) represents the time delay direction vector, ω τ =2πΔfτ represents the distance-in-radians frequency, Δf represents the subcarrier spacing, τ represents the time delay information, and N c Indicates the number of subcarriers; a d (ω D N s ) represents the Doppler direction vector, ω D =2πT OFDM f D T represents the frequency in radians of velocity. OFDM The time, f, is represented by an OFDM symbol and a cyclic prefix. D N represents the Doppler frequency shift. s This represents the number of OFDM symbols in a frame; A(m,n) represents the channel effect. This represents additive white Gaussian noise; Step 2: Multiply the sensing information matrix by its conjugate transpose to obtain the distance covariance matrix; multiply the conjugate transpose of the sensing information matrix by the sensing information matrix to obtain the velocity covariance matrix; the distance covariance matrix and velocity covariance matrix are expressed as follows: Where H represents the conjugate transpose; Step 3: Perform eigenvalue decomposition on the distance covariance matrix and the velocity covariance matrix to obtain their respective eigenvalues ​​and corresponding eigenvectors sorted from largest to smallest; the eigenvalues ​​and corresponding eigenvectors of each matrix are expressed as follows: in, By R r The eigenvectors are composed of, Represents R r eigenvectors, By R r The eigenvalues ​​are composed of, Represents R r The eigenvalues ​​of vector x are represented by diag(x), which is a diagonal matrix containing the elements of vector x. By R V The eigenvectors are composed of, By R V The eigenvalues ​​are composed of; Step 4: The eigenvector corresponding to the largest eigenvalue among the sorted eigenvalues ​​of the distance and velocity covariance matrices is used as the signal subspace, and the rest as the noise subspace; the signal subspace and noise subspace are respectively represented as: E s =[S1] E s ′ =[S ′ 1] Among them, E s E n E represents the signal subspace and noise subspace of the distance, respectively. s ′ E ′ n The signal subspace and noise subspace represent velocity, respectively; Step 5: Calculate the distance spectrum function and velocity spectrum function, and obtain the distance and velocity information of the target object through spectral peak search.

2. The two-dimensional multi-signal classification and sensing method based on orthogonal frequency division multiplexing signals according to claim 1, characterized in that, In step five, the distance spectrum function and velocity spectrum function are calculated, and the distance and velocity information of the target object are obtained through spectral peak search. The distance spectrum function and velocity spectrum function are expressed as follows: in, k r ∈[0,k max_r -1]; k v ∈[0,k max_v -1];k max_r and k max_v k represents the number of virtual snapshots for distance and speed, respectively. r Represents ω τ The corresponding index, k v Represents ω D The corresponding index; the distance and velocity information of the target object are respectively represented as: Where C0 represents the speed of light, f c Indicates the carrier frequency.

3. A system for a two-dimensional multiple signal classification and sensing method based on orthogonal frequency division multiplexing (OFDM) signals, used to implement the two-dimensional multiple signal classification and sensing method based on OFDM signals as described in any one of claims 1-2, the system comprising: The signal generation module is used at the transmitting end to generate an integrated orthogonal frequency division multiplexing (OFDM) signal, which is then transmitted to free space via the transmitting antenna. The signal receiving module is used at the receiving end to obtain the distance and velocity information of the target object by using a two-dimensional multi-signal classification and sensing method for the echo signal reflected by the target object.

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