Multi-beam array interference suppression method combining time-frequency analysis and symbol characteristics

Abstract

The invention relates to a multi-beam array interference suppression method combining time-frequency analysis and symbol characteristics, and belongs to the field of array interference suppression. The method comprises the following steps: carrying out fast Fourier transform on a received signal after sampling and truncation, converting the received signal into a time-frequency domain, calculating a sampling covariance matrix, calculating a projection matrix, carrying out interference suppression, and carrying out inverse fast Fourier transform after redistributing subcarriers; respectively synchronizing the first path and the second path of the expected signal; extracting a two-path signal by using the cyclic characteristic of the cyclic prefix, and carrying out OFDM demodulation and frequency domain channel estimation on the extracted signal; and calculating a weight according to a maximum signal-to-noise ratio criterion, and weighting the two-path signal to obtain an output signal. According to the multi-beam array interference suppression method combining time-frequency analysis and symbol characteristics, strong interference is suppressed in a time-frequency domain by using an orthogonal projection algorithm, two relatively strong paths are extracted by using the cyclic characteristic of a cyclic prefix, and the two paths are combined by using a maximum signal-to-noise ratio criterion, so that the reliability of a receiving end can be effectively improved.

Description

Technical Field

[0001] The invention belongs to the field of array interference suppression, and relates to a multi-beam array interference suppression method combining time-frequency analysis with symbol characteristics. Background Art

[0002] In recent years, with the rapid development of communication, materials, navigation, automation, aero-engines and other fields, the development of unmanned aerial vehicles (UAVs) has attracted widespread attention from countries around the world. Compared with manned aircraft, UAVs have the advantages of low cost, simple operation, flexible configuration, strong environmental adaptability, and the ability to complete specific tasks under specific circumstances, so their application scope is gradually expanding. From high-altitude aerial photography, geographic surveying and mapping, disaster warning and assessment, to environmental monitoring, emergency rescue, disaster relief and other diversified application scenarios, drones are changing people's lives and working methods in unprecedented ways, demonstrating their immeasurable application prospects and social value.

[0003] As the electromagnetic spectrum environment becomes increasingly complex, the challenges faced by drone data links in terms of anti-interference include path loss caused by long-distance information transmission, fading caused by obstacles in the propagation path, and interference caused by human factors. There are many methods for drone data link anti-interference. Among them, the method of improving the receiving end of drone data link is flexible in design and can quickly improve the anti-interference ability of drone data link in a short period of time. In addition, the design of anti-interference module at the receiving end has stronger adaptability to the protocol and does not require modification of existing standards.

[0004] With the development of array antenna technology, adaptive array has become a key technology for the new generation of data link anti-interference receivers. In order to meet the needs of broadband transmission, it is often used with orthogonal frequency division multiplexing (OFDM) signals. There are two typical array interference suppression technologies: one is the beamforming technology based on smart antennas, which has strict requirements on antenna arrays. The array manifold of the antenna is regular, and the spacing of the antenna elements is uniform, generally λ / 2, that is, half a wavelength. The other is the multiple-input multiple-output (MIMO) beamforming technology, which requires a large antenna spacing, but has no requirements for array manifolds, and has great advantages in dealing with signal multipath fading problems. In a multipath propagation environment, the power of the first path of the desired signal is not always much higher than the second path, which means that the application value of the second path is equally important. However, the current array interference suppression technology mainly focuses on extracting the main path of the desired signal, while ignoring other paths and not making full use of these paths.

[0005] Therefore, in order to fully utilize the multipath of the desired signal while suppressing strong interference and improve the signal-to-noise ratio at the receiving end, it is necessary to propose a multi-beam array interference suppression method that combines time-frequency analysis and symbol characteristics to improve the anti-interference ability of the receiver. Summary of the invention

[0006] In view of the above background, the present invention takes into account the multipath strong confrontation scenario in which both the high-power broadband suppression interference signal and the desired signal in the downlink data link of the drone enter the receiver through the multipath channel, and designs a multi-beam array interference suppression method that combines time-frequency analysis with symbol features. The present invention mainly solves two problems. One is to convert the signal to the time-frequency domain, and use the orthogonal projection algorithm to suppress the strong interference signal passing through the multipath channel; the second is to use the cyclic characteristics of the cyclic prefix (CP) to extract the two stronger paths, and then use the maximum signal-to-noise ratio merging to merge the two paths to enhance beamforming and improve the output signal-to-noise ratio. Therefore, the purpose of the present invention is to provide a multi-beam array interference suppression method that combines time-frequency analysis with symbol features.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A multi-beam array interference suppression method combining time-frequency analysis and symbol characteristics, characterized in that the method comprises the following steps:

[0009] Step 1: Sample and truncate the received signal of each channel respectively, and then perform Fast Fourier Transform (FFT) to convert the time domain signal to the time-frequency domain for analysis, and calculate the sampling covariance matrix, use the Second Order Statistic of Eigenvalues ​​(SORTE) estimation algorithm to estimate the orthogonal complementary space projection matrix of the interference signal in the molecular band, perform subspace interference suppression in the molecular band, redistribute the subcarriers of the signal after interference suppression, and then perform Inverse Fast Fourier Transform (IFFT) to convert it into a time domain signal;

[0010] Step 2: Perform time-frequency synchronization operations on the time domain signals of different channels respectively, and synchronize them to the head of the first path and the head of the second path of the desired signal respectively, to generate two sets of sequences;

[0011] Step 3: Use the cyclic characteristics of CP to extract the signals of the two stronger paths of the desired signal, perform OFDM demodulation on the extracted two path signals to obtain frequency domain signals, use pilot symbols to perform frequency domain channel estimation on each extracted path signal and obtain the channel estimation vector through interpolation;

[0012] Step 4: Calculate the weights based on the maximum signal-to-noise ratio criterion and the channel estimation vector obtained in step 3, and combine the two path signals in the frequency domain by sub-band to obtain the final output signal.

[0013] Furthermore, in step 1, since the data link uses OFDM broadband waveforms and the multipath channels of the received signals on different antennas are different, in order to better represent the frequency selective fading of the channel, it is necessary to distinguish the channel and the signal in the frequency dimension into many sub-bands for separate analysis, so that the channel on each sub-band can be approximated as flat fading. When the number of receiving antennas is M, the broadband array receiving signal model for multipath environment after FFT of the received signal is:

[0014]

[0015] Where m∈{1,2,…,M},x m (k,l) represents the signal on the kth subcarrier of the lth OFDM symbol received by the mth antenna; represents the frequency domain channel response of the desired signal to the kth subcarrier of the lth OFDM symbol of the mth receiving antenna; represents the frequency domain channel response of the interference signal to the kth subcarrier of the lth OFDM symbol of the mth receiving antenna; s(k,l) represents the transmitted signal on the kth subcarrier of the lth OFDM symbol; J(k,l) represents the interference signal on the kth subcarrier of the lth OFDM symbol; N m (k, l) represents the additive white Gaussian noise on the mth antenna.

[0016] Furthermore, the array receiving signal model is written as a matrix operation:

[0017] x(k,l)=H1(k,l)s(k,l)+H2(k,l)J(k,l)+N(k,l).(2)Wherein, A signal vector is received by the array; is the frequency domain channel vector of the desired signal; is the interference signal frequency domain channel vector; is the noise signal vector.

[0018] The specific form of x(k,l) is

[0019] x(k,l)=[x1(k,l),x2(k,l),…,x M (k,l)] T (3)

[0020] The specific expression of H1(k,l) is

[0021]

[0022] The specific form of H2(k,l) is

[0023]

[0024] The specific form of N(k,l) is

[0025] N(k,l)=[N1(k,l),N2(k,l),…,N M (k,l)] T (6)

[0026] Before the orthogonal projection algorithm starts, the covariance matrix of the kth subband of the received signal needs to be calculated.

[0027] The covariance matrix is ​​defined as

[0028]

[0029] in, Indicates the mean. Since the data obtained is discrete sampling data, the sampling covariance matrix is ​​used instead of the covariance matrix, that is, the sampling covariance matrix of the kth subband is expressed as

[0030]

[0031] Wherein, L is the number of OFDM symbols required for calculation.

[0032] Since OFDM signal, interference signal and noise signal are statistically independent of each other, the sampling covariance matrix It can be expressed as

[0033]

[0034] Among them, R s (k) represents the covariance matrix of the kth subband of the OFDM signal; R J (k) represents the covariance matrix of the kth subband of the interference signal; R n (k) represents the covariance matrix of the kth subband of the noise signal. Since the interference signal strength is much greater than the OFDM signal and noise signal strength, the above formula is further simplified to

[0035]

[0036] Among them, R w (k) = R s (k)+R n (k). For the matrix Perform eigenvalue decomposition to obtain

[0037]

[0038] in, represents the sampling covariance matrix Non-zero eigenvalues ​​of ; U stands for The eigenvalues ​​of correspond to the unitary matrix composed of eigencolumn vectors, satisfying the property UU H =I. When the interference signal strength is much greater than the OFDM signal and the noise signal, these Q eigenvalues ​​are called main eigenvalues. The eigenvectors corresponding to these Q eigenvalues ​​form the interference signal subspace, denoted as S J =[e1,e2,…,e Q ], where e i (i=1,2,…,Q) represents the sampling covariance matrix The eigenvectors corresponding to the Q large eigenvalues ​​of .

[0039] The value of Q is affected by the number of interference signal sources, so the present invention uses the variance information of the eigenvalue second-order statistics to construct a decision function for estimating the number of signal sources. Combined with S J =[e1,e2,…,e Q ], the subspace of the interference signal can be estimated, and then the orthogonal complementary space projection matrix of the interference signal can be obtained as

[0040]

[0041] Where I is the M×M unit matrix. Projecting the received signal vector x(k,l) onto the orthogonal complement space of the interference signal yields

[0042]

[0043] Among them, s(k,l)=H1s(k,l),J(k,l)=H2J(k,l). Considering The above formula can be simplified to

[0044]

[0045] The above formula shows that the strong interference signal can be eliminated by projecting the received signal vector into the orthogonal complement space of the interference signal.

[0046] Since orthogonal projection interference suppression does not combine multiple signals, it is necessary to reassemble the sub-band data after interference suppression into M frequency domain signals. Perform IFFT on the M signals respectively to convert the frequency domain signals into time domain signals. Define two new signal vectors

[0047] z(j,i)=[z1[(j1)·N sym+i],…,z M [(j1)·N sym +i]] T (15)

[0048]

[0049] Among them, N sym Represents the number of sampling points of a complete OFDM symbol, N CP Represents the number of sampling points of the OFDM symbol cyclic prefix, N u represents the number of sampling points of the OFDM symbol excluding the cyclic prefix, and N sym =N u +N CP .z m [(j-1)N sym +i] represents the i-th sample value of the j-th OFDM symbol in the m-th channel; z(j,i) represents the signal vector composed of the i-th sample value of the j-th OFDM symbol in all M channels; z(j,i+N u ) represents the i+Nth OFDM symbol of the jth OFDM symbol of all M channels u The signal vector is composed of sampling points.

[0050] Furthermore, in step 2, each channel is synchronized in time and frequency using the synchronization sequence, and is synchronized to the positions of the first path and the second path respectively, so as to obtain two groups of sequences with different synchronization positions.

[0051] Furthermore, in step 3, the two sets of sequences after time-frequency synchronization are used to form multi-beams using the cyclic characteristics of CP, and the first path and the second path are taken out respectively. Taking the first path as an example, the optimization objective function is established

[0052]

[0053] stω H ω=1

[0054] in, i=1,2,…,N g ,j=1,…,N, further define

[0055]

[0056] The optimization function is further expressed as

[0057]

[0058] Constructing performance functions using Lagrange multiplier method

[0059] L(ω)=ω H Rω-μ(ωH ω-1) (20)

[0060] Yes * Taking the derivative and setting it to zero gives

[0061]

[0062] Rω=μω

[0063] From the above formula, we can see that ω is the right eigenvector corresponding to the eigenvalue of matrix R. H ω=1, so

[0064] ω H Rω=μ, (22)

[0065] The original objective function is to make |ω H Rω| reaches the maximum, so the optimal weight ω is the right eigenvector corresponding to the eigenvalue with the largest modulus of the matrix R. Weighting the sequence synchronized to the head of the first path in step 2 can extract the first path Right now

[0066]

[0067] Among them, z1 represents the sequence synchronized to the first path header in step 2. Similarly, we can perform the same operation as step 3 based on the sequence synchronized to the second path header in step 2 to obtain the second path The two signals are demodulated by OFDM respectively, and then the pilot symbols in the frame structure are used to estimate the channels of the two signals respectively and the channel gains H1 and H2 are obtained by interpolation.

[0068] Furthermore, in step 4, the two signals are divided into subbands in the frequency domain and combined using maximum signal-to-noise ratio combining. Specifically, the output signal of the combining module can be written as

[0069]

[0070] Among them, ω m (k, l) represents the weighting factor of the diversity branch of the kth subband. Finally, the combined output signal is input into the remaining communication demodulation module for further processing.

[0071] Beneficial Effects

[0072] The beneficial effects of the present invention are: a multi-beam array interference suppression method that combines time-frequency analysis with symbol features, transforms time domain signals into time-frequency domains and uses an orthogonal projection algorithm to suppress strong interference, which can effectively suppress interference signals passing through multipath channels. The two stronger paths in the OFDM signal are extracted using the cyclic characteristics of the CP in the OFDM signal, and then the two paths are combined using the maximum signal-to-noise ratio combination. This method improves the diversity gain of the receiving end, can effectively improve the signal-to-noise ratio, and improves the reliability of the receiving end. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to make the purpose, technical solution and beneficial effects of the present invention more clear, the present invention provides the following drawings for illustration:

[0074] Figure 1 This is a diagram of the array interference suppression framework of the present invention;

[0075] Figure 2 This is a schematic diagram of taking the first path by using CP according to the present invention;

[0076] Figure 3 This is a schematic diagram of using CP to obtain a second path according to the present invention; DETAILED DESCRIPTION

[0077] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0078] Consider a Long Term Evolution (LTE) drone downlink data link scenario with a single broadband suppression interference source. The waveform adopts an OFDM waveform, where the receiver is configured with M receiving antennas, the desired signal transmitter and the interference signal transmitter are each configured with 1 transmitting antenna, and the downlink data signal and the interference signal enter the receiver through different multipath channels.

[0079] like Figure 1 As shown, the present invention provides a multi-beam array interference suppression method combining time-frequency analysis and symbol characteristics, which mainly includes the following steps:

[0080] Step 1: Since the data link uses OFDM broadband waveforms and the received signals on different antennas pass through different multipath channels, in order to better represent the frequency selective fading of the channel, it is necessary to distinguish the channel and signal in the frequency dimension into many sub-bands for separate analysis, so that the channel on each sub-band can be approximated as flat fading. When the number of receiving antennas is M, the broadband array receiving signal model for multipath environment after FFT of the received signal is:

[0081]

[0082] Where m∈{1,2,…,M},x m (k,l) represents the signal on the kth subcarrier of the lth OFDM symbol received by the mth antenna; represents the frequency domain channel response of the desired signal to the kth subcarrier of the lth OFDM symbol of the mth receiving antenna; represents the frequency domain channel response of the interference signal to the kth subcarrier of the lth OFDM symbol of the mth receiving antenna; s(k,l) represents the transmitted signal on the kth subcarrier of the lth OFDM symbol; J(k,l) represents the interference signal on the kth subcarrier of the lth OFDM symbol; N m (k,l) represents the additive white Gaussian noise on the mth antenna.

[0083] Furthermore, the array receiving signal model is written as a matrix operation:

[0084] x(k,l)=H1(k,l)s(k,l)+H2(k,l)J(k,l)+N(k,l). (2)

[0085] in, A signal vector is received by the array; is the frequency domain channel vector of the desired signal; is the interference signal frequency domain channel vector; is the noise signal vector.

[0086] The specific form of x(k,l) is

[0087] x(k,l)=[x1(k,l),x2(k,l),...,x M (k,l)] T (3)

[0088] The specific expression of H1(k,l) is

[0089]

[0090] The specific form of H2(k,l) is

[0091]

[0092] The specific form of N(k,l) is

[0093] N(k,l)=[N1(k,l),N2(k,l),…,,N M (k,l)] T (6)

[0094] Before the orthogonal projection algorithm starts, the covariance matrix of the kth subband of the received signal needs to be calculated.

[0095] The covariance matrix is ​​defined as

[0096]

[0097] in, Indicates the mean. Since the data obtained is discrete sampling data, the sampling covariance matrix is ​​used instead of the covariance matrix, that is, the sampling covariance matrix of the kth subband is expressed as

[0098]

[0099] Wherein, L is the number of OFDM symbols required for calculation.

[0100] Since OFDM signal, interference signal and noise signal are statistically independent of each other, the sampling covariance matrix It can be expressed as

[0101]

[0102] Among them, R s (k) represents the covariance matrix of the kth subband of the OFDM signal; R J (k) represents the covariance matrix of the kth subband of the interference signal; R n (k) represents the covariance matrix of the kth subband of the noise signal. Since the interference signal strength is much greater than the OFDM signal and noise signal strength, the above formula is further simplified to

[0103]

[0104] Among them, R w (k) = R s (k)+R n (k). For the matrix Perform eigenvalue decomposition to obtain

[0105]

[0106] in, represents the sampling covariance matrix Non-zero eigenvalues ​​of ; U stands for The eigenvalues ​​of correspond to the unitary matrix composed of eigencolumn vectors, satisfying the property UU H =I. When the interference signal strength is much greater than the OFDM signal and the noise signal, these Q eigenvalues ​​are called main eigenvalues. The eigenvectors corresponding to these Q eigenvalues ​​form the interference signal subspace, denoted as S J =[e1,e2,…,e Q ], where e i(i=1,2,…,Q) represents the sampling covariance matrix The eigenvectors corresponding to the Q large eigenvalues ​​of .

[0107] The value of Q is affected by the number of interference signal sources. The present invention uses the variance information of the eigenvalue second-order statistics to construct a decision function for estimating the number of signal sources. In the case of strong interference, the eigenvalue approximately satisfies

[0108]

[0109] definition i=1,…,M-1, then

[0110]

[0111] Define the variance of the eigenvalue as ε k ,

[0112]

[0113] Among them, k = 1, 2, ..., M-1. The observation value is constructed as

[0114]

[0115] From the above formula, we can see that the observed values ​​satisfy the following relationship

[0116]

[0117] Among them, C1 and C2 are constants. Then the decision function can be written as

[0118]

[0119] Calculated using the above formula Combined with S J =[e1,e2,…,e Q ], the subspace of the interference signal can be estimated.

[0120] Using the interference signal subspace, the orthogonal complement space projection matrix of the interference signal can be calculated as:

[0121]

[0122] Where I is the M×M unit matrix. Projecting the received signal vector x(k,l) onto the orthogonal complement space of the interference signal yields

[0123]

[0124] Among them, s(k,l)=H1s(k,l),J(k,l)=H2J(k,l). Considering The above formula can be simplified to

[0125]

[0126] The above formula shows that the strong interference signal can be eliminated by projecting the received signal vector into the orthogonal complement space of the interference signal.

[0127] Since orthogonal projection interference suppression does not combine multiple signals, it is necessary to reassemble the sub-band data after interference suppression into M frequency domain signals. Perform IFFT on the M signals respectively to convert the frequency domain signals into time domain signals. Define two new signal vectors

[0128] z(j,i)=[z1[(j1)N sym +i],…,z M [(j1)N sym +i]] T (twenty one)

[0129]

[0130] Among them, N sym Represents the number of sampling points of a complete OFDM symbol, N CP Represents the number of sampling points of the OFDM symbol cyclic prefix, N u represents the number of sampling points of the OFDM symbol excluding the cyclic prefix, and N sym =N u +N CP .z m [(j-1)N sym +i] represents the i-th sample value of the j-th OFDM symbol in the m-th channel; z(j,i) represents the signal vector composed of the i-th sample value of the j-th OFDM symbol in all M channels; z(j,i+N u ) represents the i+Nth OFDM symbol of the jth OFDM symbol of all M channels u The signal vector is composed of sampling points.

[0131] Step 2: Use the synchronization sequence to perform time-frequency synchronization on each channel, and synchronize to the positions of the first path and the second path respectively, to obtain two sets of sequences with different synchronization positions.

[0132] Step 3: Use the cyclic characteristics of CP to form multi-beams for the two sets of sequences after time-frequency synchronization, and extract the first path and the second path respectively. Take the first path as an example, Figure 2 As shown, the optimization objective function is established

[0133]

[0134] stω H ω=1

[0135] in, i=1,2,…,N CP ,j=1,…,N, further define

[0136]

[0137] The optimization function is further expressed as

[0138]

[0139] Constructing performance functions using Lagrange multiplier method

[0140] L(ω)=ω H Rω-μ(ω H ω-1) (26)

[0141] Yes * Taking the derivative and setting it to zero gives

[0142]

[0143] Rω=μω

[0144] From the above formula, we can see that ω is the right eigenvector corresponding to μ, and the eigenvalue of matrix R is ω. H get

[0145] ω H Rω=μω H ω (28)

[0146] There is also ω H ω=1, so

[0147] ω H Rω=μ, (29)

[0148] The original objective function is to make |ω H Rω| reaches the maximum, so the optimal weight ω is the right eigenvector corresponding to the eigenvalue with the largest modulus of the matrix R. Weighting the sequence synchronized to the head of the first path in step 2 can extract the first path Right now

[0149]

[0150] Among them, z1 represents the sequence synchronized to the first path header in step 2. Similarly, if Figure 3 As shown, we can perform the same operation as step 3 based on the sequence synchronized to the head of the second path in step 2 to obtain the second path The two signals are demodulated by OFDM respectively, and then the pilot symbols in the frame structure are used to estimate the channels of the two signals respectively and the channel gains H1 and H2 are obtained by interpolation.

[0151] Step 4: Divide the two signals into subbands in the frequency domain and combine them using maximum signal-to-noise ratio merging. Specifically, the output signal of the merging module can be written as

[0152]

[0153] Among them, ω m (k, l) represents the weighting factor of the diversity branch of the kth subband. Since the interference signal has been eliminated in the orthogonal projection interference suppression module, It can be expressed as

[0154]

[0155] Among them, H m (k,l) is the frequency domain response of the desired signal. Combining the above two equations, we can get

[0156]

[0157] The received signal-to-noise ratio can be

[0158]

[0159] Among them, E s is the expected signal power, is the noise signal power. According to the Cauchy-Schwarz inequality, we can get

[0160]

[0161] Observing the above formula, we can find that when When , the inequality becomes an equality, and the maximum signal-to-noise ratio is obtained by combining the outputs. Then the weights need to be normalized to obtain the final weights.

[0162]

[0163] The resulting output signal is

[0164]

[0165] Finally, the combined output signal is input into the remaining communication demodulation module for further processing.

[0166] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.

Claims

1. A multi-beam array interference suppression method combining time-frequency analysis and symbol characteristics, characterized in that: The method comprises the following steps: Step 1: Sample and truncate the received signal of each channel respectively, and then perform Fast Fourier Transform (FFT) to convert the time domain signal to the time-frequency domain, calculate the sampling covariance matrix, use the Second Order Statistic of Eigenvalues ​​(SORTE) estimation algorithm to estimate the orthogonal complementary space projection matrix of the interference signal in the molecular band, and perform subspace interference suppression in the molecular band, redistribute the subcarriers of the signal after interference suppression, and then perform Inverse Fast Fourier Transform (IFFT) to convert it into a time domain signal; Step 2: Perform time-frequency synchronization operations on the time domain signals of different channels obtained in step 1, respectively, and synchronize to the head of the first path and the head of the second path of the desired signal, to generate two sets of sequences; Step 3: Use the cyclic characteristics of the cyclic prefix (CP) to extract the signals of the two stronger paths of the desired signal, perform orthogonal frequency division multiplexing (OFDM) demodulation on the two stronger path signals to obtain frequency domain signals, use pilot symbols to perform frequency domain channel estimation on each extracted path signal, and obtain a channel estimation vector by interpolation; Step 4: Calculate the weights based on the maximum signal-to-noise ratio criterion and the channel estimation vector obtained in step 3, and combine the two path signals in the frequency domain by sub-band to obtain the final output signal.

2. The multi-beam array interference suppression method combining time-frequency analysis and symbol features according to claim 1, characterized in that: In step 1, the received signal is distinguished by IFFT in the frequency domain for separate analysis of multiple sub-bands. When the number of receiving antennas is M, the array received signal is written as x(k,l)=H1(k,l)s(k,l)+H2(k,l)J(k,l)+N(k,l), (1) in, A signal vector is received by the array; is the frequency domain channel vector of the desired signal; is the interference signal frequency domain channel vector; For the noise signal vector, calculate the sampling covariance matrix of the kth subband of the received signal Among them, L is the number of OFDM symbols required for calculation, and the matrix Perform eigenvalue decomposition to obtain in, represents the sampling covariance matrix Non-zero eigenvalues ​​of ; U stands for The eigenvalues ​​of correspond to the unitary matrix composed of eigencolumn vectors, satisfying UU H =I, since the interference signal strength is much greater than the OFDM signal and the noise signal, the eigenvectors corresponding to these Q eigenvalues ​​form an interference signal subspace, denoted as S J =[e1,e2,…,e Q ], the value of Q is affected by the number of interference signal sources. The present invention uses the variance information of the eigenvalue second-order statistics to construct a decision function for estimating the number of signal sources. In the case of strong interference, the eigenvalue approximately satisfies The obtained eigenvalues ​​are used to estimate the interference orthogonal complement space projection matrix through the SORTE algorithm. First, define Define the variance of the eigenvalue as ε k Among them, k = 1, 2, ..., M-1. The observation value is constructed as From the above formula, we can see that the observed values ​​satisfy the following relationship Among them, C1, C2 are constants, so the decision function can be written as Using the calculated Combined with S J =[e1,e2,…,e Q ], get the orthogonal complement space projection matrix of the interference signal Project the received signal vector x(k,l) onto the orthogonal complement space of the interference signal to obtain The interference suppressed sub-band data is reassembled into M frequency domain signals, and IFFT is performed on the M signals respectively to convert the frequency domain signals into time domain signals, and two time domain signal vectors are defined z(j,i)=[z1[(j-1)·N sym +i],…,z M [(j-1)·N sym +i]] T (11) Among them, N sym Represents the number of sampling points of a complete OFDM symbol, N CP Indicates the number of sampling points of the OFDM symbol cyclic prefix, N u represents the number of sampling points excluding the cyclic prefix, and N sym =N u +N CP , z m [(j-1)·N sym +i] represents the ith sample value of the jth OFDM symbol in the mth channel; z(j,i) represents the signal vector composed of the ith sample value of the jth OFDM symbol in all M channels; z(j,i+N u ) represents the i+Nth OFDM symbol of the jth OFDM symbol of all M channels u The signal vector is composed of sampling points.

3. The multi-beam array interference suppression method combining time-frequency analysis and symbol features according to claim 1, characterized in that: In step 2, the synchronization sequence is extracted, and time-frequency synchronization is performed on each channel respectively, and the positions of the first path and the second path are synchronized respectively, so as to obtain two groups of sequences with different synchronization positions.

4. The multi-beam array interference suppression method combining time-frequency analysis and symbol features according to claim 1, characterized in that: In step 3, the cyclic characteristics of CP are used to form multiple beams, and the first path and the second path are extracted respectively. The first path is taken as an example to establish the maximum correlation objective function. in, i=1,2,…,N CP , j = 1, ..., N, and the Lagrange multiplier method is used to solve and obtain the optimal weight ω. The first path synchronized can be extracted by weighting the first path. Among them, z1 represents the sequence synchronized to the first path header. Similarly, the sequence synchronized to the second path header can be extracted by performing the same operation as above. The extracted two-path signals are respectively subjected to OFDM demodulation, and then the output signal of the OFDM demodulation module is subjected to frequency domain channel estimation using pilot symbols, and the channel gains H1 and H2 are obtained by interpolation.

5. The multi-beam array interference suppression method combining time-frequency analysis and symbol features according to claim 1, characterized in that: In step 4, the two signals are divided into subbands in the frequency domain and combined by using maximum signal-to-noise ratio combining to achieve the purpose of obtaining multi-beam diversity gain. Specifically, the combined output signal can be written as Among them, ω m (k, l) represents the weighting factor of the diversity branch of the kth subband. Since the interference signal has been eliminated in the orthogonal projection interference suppression module, It can be expressed as Among them, H m (k, l) is the channel frequency domain response of the desired signal. Combining the above two equations, we can get The signal-to-noise ratio of the received signal can be written as Among them, E s is the expected signal power, is the noise signal power, combined with the Cauchy-Schwarz inequality, when The maximum signal-to-noise ratio is reached when , and then the weight is normalized to get the final weight The combined output signal is Finally, the combined output signal is input into the remaining communication demodulation module for further processing.

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