Intelligent reflecting surface assisted omni-directional single antenna receiver radiation source direction finding method

Abstract

The application belongs to the technical field of radiation source direction finding, and particularly relates to a kind of omnidirectional single antenna receiver radiation source direction finding method assisted by intelligent reflecting surface.The scheme utilizes the reconstruction ability of intelligent reflecting surface to multipath signals, compensates the inherent system limitation that omnidirectional single antenna receiver cannot estimate the direction of arrival of radiation source by designing special periodic time-varying coefficients.For slow-varying and fast-varying radiation signal sources, quasi-MUSIC spatial spectrum and LS spatial spectrum are respectively constructed to realize the estimation of the direction of radiation source.In the case of slow-varying radiation signal source, the application has similar estimation accuracy as traditional array antenna receiver;in the case of fast-varying radiation signal source, the application has relatively high accuracy although the performance is reduced to some extent.Meanwhile, since a large number of radio frequency chains do not need to be configured, the receiver also does not need to be connected with RIS, only the RIS needs to maintain the given periodically changed reflection coefficient, which greatly reduces the requirements of the equipment on hardware facilities, and has higher practicability.

Description

Technical Field

[0001] This invention belongs to the field of radiation source direction finding technology, specifically relating to a radiation source direction finding method for an omnidirectional single-antenna receiver assisted by an intelligent reflector. Background Technology

[0002] Radio direction finding is the process of determining the direction of arrival of radio waves using instruments and equipment, based on the propagation characteristics of electromagnetic waves. The direction of arrival refers to the direction in which the radio wave reaches the location of the direction finder in the actual electromagnetic environment. The ultimate goal of direction finding is usually to determine the direction of a radiation source and even its specific location. The basic principles of direction finding technology can be divided into two categories: one is to use directional antennas to receive incoming signals from various directions and determine which directions contain signal sources by comparing the signal strength in each direction with a given threshold; the other is to use the array response of an array antenna and perform spatial spectrum estimation using array signal processing techniques to determine the direction of the signal source. Direction finding using directional antennas often has lower accuracy, while the accuracy of array antennas is positively correlated with the number of antennas.

[0003] Omnidirectional single-antenna receivers, due to inherent system limitations, cannot estimate the direction of arrival of the radiation source. The introduction of Reconfigurable Intelligent Surfaces (RIS) offers a potential solution to this problem. RIS is a novel material with reconfigurable electromagnetic properties, often composed of numerous units that can dynamically change their electromagnetic parameters, enabling adjustments to the amplitude, phase, and even polarization of the reflected signal. RIS has been widely studied in the field of mobile communications due to its passive, low-cost, and flexible deployment advantages. This is because RIS can provide additional degrees of freedom in the wireless channel without altering the current wireless network infrastructure architecture, thereby improving communication quality. Therefore, how to utilize the signal manipulation capabilities of RIS to solve the problem of omnidirectional single-antenna receivers being unable to measure the direction of the radiation source is a worthy research topic. Summary of the Invention

[0004] To address the problem that omnidirectional single-antenna receivers cannot perform radiation source direction finding, this invention proposes a radiation source direction finding method for omnidirectional single-antenna receivers assisted by intelligent reflectors.

[0005] To better illustrate the present invention, the terminology and system structure used in the technical solution of the present invention will be introduced first.

[0006] AoA: Angle of Arrival.

[0007] DFT, Discrete Fourier Transform.

[0008] LS: Least Squares.

[0009] MUSIC: Multiple Signal Classification.

[0010] RIS: Reconfigurable Intelligent Surface, a smart reflective surface that can dynamically change its electromagnetic properties to adjust the amplitude, phase, and even polarization of the reflected signal.

[0011] RMSE: Root of Mean Squared Error.

[0012] ULA: Uniform Linear Array.

[0013] Figure 1 The diagram shown is a schematic of the RIS-assisted omnidirectional single-antenna receiver radiation source direction finding system of the present invention:

[0014] In this system, it is assumed that the radiation source Tx and the receiver Rx both have 1 antenna and are omnidirectional antennas; the number of RIS elements is N, and it is assumed that a horizontal uniform linear array is used. Let α0, α1, and α2 represent the complex fading coefficients of the three channels Tx-Rx, Tx-RIS, and RIS-Rx, respectively, and the azimuth angles of the three channels Tx-Rx, Tx-RIS, and RIS-Rx are respectively... as well as Therefore, the channel vectors of Tx-RIS and Rx-RIS can be expressed as:

[0015]

[0016] and

[0017]

[0018] in, Let represent the antenna array response vector of the RIS. For a horizontal uniform linear array with N elements, we have:

[0019]

[0020] In the formula, λ and Δ represent the signal wavelength and the RIS unit spacing, respectively. Consider the following periodic time-varying reflection coefficient for the RIS configuration:

[0021]

[0022] Where T represents the number of time slots contained in one RIS cycle (which can be defined as one frame), the received signal of the t-th time slot in the m-th frame can be expressed as:

[0023] y tm =[α0+g H diag(φ t )q]s tm +w tm ,

[0024] Among them, s tm ~CN(0,P) represents a radiated signal with power P, w tm ~CN(0,N0) represents the receiver noise with power N0. The received signals within each frame are stretched into a column vector, i.e., y m =[y 1m ,y 2m ,…,y Tm ] T If M frames are observed, the received signal can be represented as a matrix Y = [y1, y2, ..., y]. M ].

[0025] The technical solution adopted in this invention is as follows:

[0026] S1. Construct the RIS coefficient matrix to satisfy...

[0027]

[0028] S2. Let the received signal in the t-th time slot of the m-th frame be represented as:

[0029]

[0030] Determine the type of radiation source signal; if s 1m =s 2m =…=s Tm =s m This is a slowly changing signal, proceeding to S3; if s 1m ≠s 2m ≠…≠s Tm This is a fast-changing signal, entering S4;

[0031] S3. The received signal is further represented as follows:

[0032]

[0033] in

[0034] s T =[s1,s2,…,s M [W] tm =w tm .

[0035] To estimate the scaling channel autocorrelation matrix, specifically by left-multiplying the received signal by... The left inverse cancels out the effect of RIS, i.e.

[0036]

[0037] Therefore, the scaled channel autocorrelation matrix can be expressed as:

[0038]

[0039] When the number of frames approaches infinity, we have

[0040]

[0041] For [R] z ] 2:end.2:end Perform eigenvalue decomposition to extract the noise space, i.e.

[0042] [R z ] 2:end.2:end =UΛU H ,

[0043] Where Λ is a diagonal matrix, and the diagonal elements are [R]. z ] 2:end.2:end The eigenvalues ​​are arranged in descending order, and U is the feature matrix composed of the corresponding eigenvectors. Note that as the number of frames approaches infinity, Λ can be further expressed as:

[0044]

[0045] Therefore, the characteristic matrix corresponding to the noise space is U. n =U :,2:end .

[0046] Construct a pseudo-MUSIC spatial spectrum to estimate the azimuth angle of the Tx-RIS channel.

[0047] The MUSIC spatial spectrum is constructed based on the orthogonality of the signal subspace and the noise subspace. The angle with the lowest correlation to the noise feature matrix is ​​found by traversing the angle grid, which is the desired direction. For the scheme proposed in this invention, a quasi-MUSIC spatial spectrum can also be constructed based on the orthogonality of the channel subspace and the noise subspace for angle estimation. Specifically, the quasi-MUSIC spatial spectrum is expressed as:

[0048]

[0049] Therefore, the azimuth angle of the Tx-RIS channel is the angle corresponding to the peak value of the pseudo-MUSIC spatial spectrum, that is:

[0050]

[0051] S4. The received signal is further represented as follows:

[0052]

[0053] Among them, [S] tm =s tm .

[0054] Estimate the autocorrelation matrix of the received signal, i.e.:

[0055]

[0056] As the number of frames approaches infinity, we have:

[0057]

[0058] Based on the properties of the RIS coefficient matrix, for R y Standardization is carried out, specifically as follows:

[0059]

[0060] Therefore, as the number of frames approaches infinity, we have:

[0061]

[0062] in In particular, when the distance between Tx and Rx is much greater than the distance between RIS and Rx, we have |α0|≈|α1|, and in this case, β≈|α2|e j∠β .

[0063] Construct the LS spatial spectrum and estimate the Tx-RIS channel azimuth. Note R' y It's about β and The function, therefore for any given According to R' y The LS estimate of β is obtained, i.e.:

[0064]

[0065] in

[0066]

[0067] Therefore, for a given The LS spatial spectrum can be represented as:

[0068]

[0069] The Tx-RIS channel azimuth angle is the angle corresponding to the peak value of the LS spatial spectrum, that is:

[0070]

[0071] The beneficial effects of this invention are as follows:

[0072] This invention proposes a method for radiator direction finding using an omnidirectional single-antenna receiver with intelligent reflector-assisted reflection. This scheme utilizes the reconstruction capability of intelligent reflectors for multipath signals and overcomes the inherent system limitation of omnidirectional single-antenna receivers in estimating the direction of arrival of radiating sources by designing special periodically time-varying coefficients. For slowly varying and rapidly varying radiating signal sources, quasi-MUSIC and LS spatial spectra are constructed respectively, enabling the estimation of the radiating source direction. In the case of slowly varying radiating signal sources, this invention achieves estimation accuracy similar to that of traditional array antenna receivers; in the case of rapidly varying radiating signal sources, although performance decreases slightly, high accuracy is still maintained. Furthermore, since it eliminates the need for a large RF chain and the receiver does not need to be connected to a RIS (Reflection Array Array), only requiring the RIS to maintain a periodically varying reflection coefficient, the hardware requirements are significantly reduced, making the device more practical. Attached Figure Description

[0073] Figure 1 This is a schematic diagram of the intelligent reflector-assisted omnidirectional single-antenna receiver radiation source direction finding system proposed in this invention;

[0074] Figure 2 The simulation results of the spatial spectrum under slow-varying and fast-varying radiation signal sources are as follows: (a) is a comparison between the pseudo-MUSIC spatial spectrum of the present invention and the MUSIC spatial spectrum of the conventional array antenna receiver under the slow-varying radiation signal source, and (b) is the LS spatial spectrum of the present invention under the fast-varying radiation signal source.

[0075] Figure 3 Simulation results for angle estimation accuracy under a rapidly varying radiation signal source: where (a) and (b) are the LS spatial spectra of the present invention under two rounds of random angles. Detailed Implementation

[0076] The technical solution of the present invention has been described in detail in the invention summary section. The practicality of the present invention will be explained below with reference to the accompanying drawings and simulation examples.

[0077] Figure 2-3 The simulation conditions used Gaussian white noise with power N0 = -120dBm, a RIS with 63 elements N, a RIS cycle period (frame length) T = 64, and the (n+1)th row of the T-order DFT matrix as the time-varying coefficient vector of the nth element of the RIS. The receiver was located at (0,0), the RIS was located at (0,1m), and the angle search grid was 1 degree. The conventional array receiver had 63 antennas located at (0,1m).

[0078] Figure 2The radiation source is located at (2000m, 2000m), with a transmission power P = 1W. Under a slowly varying signal source, the number of snapshots M = 100; under the same slowly varying signal source, the number of snapshots M = 100 and 1000. Since the rate of change of the signal source has no significant impact on the results for traditional array receivers, only... Figure 2 The results are given when the number of sampling points is MT. Figure 2 The spatial spectrum results for slowly varying and rapidly varying signal sources are presented separately. As can be seen from the figures, the RIS-assisted direction finding method exhibits a significant sharp peak at the target angle. For slowly varying signal sources, its performance is similar to that of a traditional array receiver, with the peak value almost always equal to 1, and the difference between the peak value and the plateau is approximately 80 dB (compared to approximately 90 dB for a traditional array receiver). For rapidly varying signal sources, the detection performance suffers a significant loss, with the plateau rising to around -22.5 dB and the peak value decreasing to around -21 dB (M=100) and -10 dB (M=1000), but still exhibiting a relatively sharp peak.

[0079] Figure 3 polar coordinates of the radiation source Where ψ∈R 6 , ψ i (i = 1, 2, ..., 6) follow a uniform distribution from 30(i-1) to 30i, and the number of snapshots M = 1000. Figure 3 The figures show the simulation results under two rounds of random angles. As can be seen from the figures, under rapidly changing signal sources, the proposed scheme exhibits relatively sharp peaks for signal sources in different directions, thus demonstrating strong robustness.

[0080] As can be seen, the intelligent reflector-assisted omnidirectional single-antenna receiver radiation source direction finding system proposed in this invention achieves estimation accuracy similar to that of traditional array antenna receivers in the case of slowly varying radiation signal sources. While performance decreases somewhat in the case of rapidly varying radiation signal sources, it still maintains high estimation accuracy, overcoming the limitation of omnidirectional single-antenna receivers in estimating the direction of radiation sources. Furthermore, the RIS-assisted direction finding proposed in this invention eliminates the need for a large number of RF chains as required by traditional array antenna receivers, and the receiver does not need to be connected to the RIS. The RIS only needs to maintain a periodically varying reflection coefficient, significantly reducing the hardware requirements and enhancing its practicality.

Claims

1. An intelligent reflecting surface assisted omni-directional single antenna receiver radiation source direction finding method, characterized in that, Comprising the following steps: S1. Constructing the coefficient matrix of the intelligent reflecting surface RIS satisfies ΦΦ T = T I N , sum(Φ, row) = 0 N , The number of RIS units is N, a uniform linear array is adopted, T represents the number of time slots contained in one RIS cycle, and satisfies: S2. Let the received signal of the mth frame and the tth time slot be represented as: y tm = [Δ0+ g H diag(φ t )q]s tm +w tm , where s tm ~CN(0, P) is the radiated signal with power P, w tm ~CN(0, N0) is the receiver noise with power N0, the number of antennas of the radiated source Tx and the receiver Rx are both 1, and an omnidirectional antenna is used; Δ0 represents the complex fading coefficient of the Tx-Rx channel, and the complex channels of Tx-RIS and RIS-Rx are And where Δ1 and Δ2 are the corresponding complex fading coefficients, and is the corresponding azimuth angle, denotes the antenna array response vector of the RIS; for a uniform linear array with N elements, there is where λ and Δ represent the signal wavelength and the RIS element spacing, respectively; the received signals in each frame are pulled into a column vector, i.e., y m = [y 1m ,y 2m ,…,y Tm ] T , and M frames of observations are made, and the received signals are represented as a matrix Y = [y1,y2,…,y M ] Determine the signal type of the radiation source. If s 1m = s 2m =... = s Tm = s m then it is a slow varying signal, go to S3; if s 1m ≠ s 2m ≠... ≠ s Tm then it is a fast varying signal, go to S4; S3, The received signal is further represented as: Wherein s T = [s1, s2,..., s M ], [W] tm = w tm The scaling channel autocorrelation matrix is estimated, specifically: the left inverse of RIS is removed by multiplying the received signal by the left inverse of RIS, i.e., Therefore, the channel autocorrelation matrix is represented as: When the number of frames tends to infinity, there is To [R z ] 2:end.2:end Eigenvalue decomposition is performed to extract the noise space, i.e. [R z ] 2:end.2:end = UΛU H , where Λ is a diagonal matrix, and the diagonal elements are [R z ] 2:end.2:end The eigenvalues of Λ are arranged in descending order, and U is a characteristic matrix composed of corresponding eigenvectors. When the frame number tends to infinity, Λ is further expressed as: Thus, the feature matrix corresponding to the noise space is U n = U :,2:end ; Constructing a quasi-MUSIC spatial spectrum to estimate the Tx-RIS channel azimuth: Therefore, the Tx-RIS channel azimuth is the angle corresponding to the peak value of the quasi-MUSIC spatial spectrum, that is: End of direction finding; S4. The received signal is further represented as: wherein [S] tm = s tm ; Estimating the autocorrelation matrix of the received signal, that is: When the number of frames tends to infinity, there is: According to the properties of the RIS coefficient matrix, the RIS coefficient matrix is normalized, specifically as follows: y ​ Therefore, when the number of frames tends to infinity, there is: wherein When the distance of Tx-Rx is much larger than the distance of RIS-Rx, there is |Δ0|≈|Δ1|, and at this time β≈|Δ2|e j∠β ; Construct LS spatial spectrum, estimate the Tx-RIS channel azimuth, note that R' y is a function of β and , so for any given the LS estimate of β can be obtained from R' y , i.e. Wherein Thus for a given The LS spatial spectrum is represented as: The Tx-RIS channel azimuth is the angle corresponding to the peak value of the LS spatial spectrum, that is:

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