A Multibeam Array Interference Suppression Method Combining Time-Frequency Analysis and Symbol Characteristics

Abstract

This invention relates to a multi-beam array interference suppression method combining time-frequency analysis and symbol characteristics, belonging to the field of array interference suppression. The method includes: sampling and truncating the received signal, performing a Fast Fourier Transform (FFT) to transform it to the time-frequency domain and calculating the sampling covariance matrix; calculating the projection matrix and performing interference suppression; reallocating subcarriers and performing an inverse FFT; synchronizing the first and second paths of the desired signal respectively; extracting the two path signals using the cyclic characteristics of the cyclic prefix; performing OFDM demodulation and frequency domain channel estimation on the extracted signals; calculating weights according to the maximum signal-to-noise ratio (SNR) criterion; and weighting the two path signals to obtain the output signal. This invention proposes a multi-beam array interference suppression method combining time-frequency analysis and symbol characteristics. It uses an orthogonal projection algorithm in the time-frequency domain to suppress strong interference, extracts the two stronger paths using the cyclic characteristics of the cyclic prefix, and merges the two paths using the maximum SNR criterion, effectively improving the reliability of the receiver.

Description

Technical Field

[0001] This invention belongs to the field of array interference suppression and relates to a multi-beam array interference suppression method that combines time-frequency analysis and symbol characteristics. Background Technology

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

[0003] With the increasing complexity of the electromagnetic spectrum environment, UAV data links face challenges in terms of anti-interference, including path loss due to long-distance information transmission, fading caused by obstacles in the propagation path, and interference from human factors. There are many anti-interference methods for UAV data links. Among them, improving the receiver end of the UAV data link offers flexible design and can quickly enhance the anti-interference capability of the UAV data link in the short term. Furthermore, designing an anti-interference module at the receiver end provides stronger adaptability to protocols without requiring modifications to existing standards.

[0004] With the development of array antenna technology, adaptive arrays have become a key technology for next-generation data link anti-jamming receivers. To meet the demands of broadband transmission, they are often used in conjunction with Orthogonal Frequency Division Multiplexing (OFDM) signals. There are two typical array interference suppression techniques: one is beamforming technology based on smart antennas, which has strict requirements for the antenna array, requiring a regular array manifold and uniform spacing between antenna elements, typically λ / 2 (half a wavelength). The other is Multiple-Input Multiple-Output (MIMO) beamforming technology, which requires a larger antenna spacing but has no requirements on array manifold, and has a significant advantage in handling multipath fading. In multipath propagation environments, the power of the first path of the desired signal is not always much higher than that of the second path, meaning the second path is equally important. However, current array interference suppression techniques mainly focus on extracting the main path of the desired signal, neglecting other paths and failing to fully utilize them.

[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 receiver, it is necessary to propose a multi-beam array interference suppression method that combines time-frequency analysis and symbol characteristics to improve the receiver's anti-interference capability. Summary of the Invention

[0006] Given the above background, this invention considers the scenario of strong multipath interference in the UAV downlink data link, where both high-power broadband suppression interference signals and desired signals enter the receiver through multipath channels. It designs a multi-beam array interference suppression method that combines time-frequency analysis and symbol characteristics. This invention mainly addresses two problems: first, converting the signal to the time-frequency domain and using an orthogonal projection algorithm to suppress strong interference signals passing through multipath channels; second, utilizing the cyclic characteristics of the cyclic prefix (CP) to extract the two strongest paths, and then using maximum signal-to-noise ratio (SNR) combining to merge the two paths, thereby enhancing beamforming and improving the output SNR. Therefore, the purpose of this invention is to provide a multi-beam array interference suppression method that combines time-frequency analysis and symbol characteristics.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

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

[0009] Step 1: Sample and truncate the received signal for each channel, then perform a Fast Fourier Transform (FFT) to convert the time-domain signal to the time-frequency domain for analysis. Calculate the sampling covariance matrix, use the Second Order Statistic of Eigenvalues ​​(SORTE) estimation algorithm to estimate the orthogonal complement projection matrix of the interference signal in the sub-band, perform sub-space interference suppression in the sub-band, redistribute the subcarriers of the interference-suppressed signal, and then perform an Inverse Fast Fourier Transform (IFFT) to convert it to 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 beginning of the first path and the beginning of the second path of the desired signal respectively, generating two sets of sequences;

[0011] Step 3: Utilize the cyclic characteristics of CP to extract the signals of the two stronger paths of the desired signal. Demodulate the extracted two path signals using OFDM to obtain the 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 merge the two path signals in the frequency domain to obtain the final output signal.

[0013] Furthermore, in step 1, since the data link utilizes OFDM wideband 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 analyze the channel and signal separately in the frequency dimension, dividing them into many sub-bands. This allows the channel in each sub-band to approximate flat fading. With M receiving antennas, the model of the wideband array receiving signal for multipath environments after FFT is as follows:

[0014]

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

[0016] Furthermore, the array received signal model is written in matrix operation form as follows:

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

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

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

[0020] The specific representation of H1(k,l) is as follows:

[0021]

[0022] The specific representation of H2(k,l) is as follows:

[0023]

[0024] The specific representation of N(k,l) is as follows:

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

[0026] Before the orthogonal projection algorithm begins, the covariance matrix of the k-th sub-band of the received signal needs to be calculated.

[0027] The covariance matrix is ​​defined as

[0028]

[0029] in, This indicates the calculation of the mean. Since the obtained data is discrete sampled data, the sampling covariance matrix is ​​used instead of the covariance matrix. That is, the sampling covariance matrix of the k-th sub-band is expressed as:

[0030]

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

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

[0033]

[0034] Among them, R s (k) represents the covariance matrix of the k-th subband of the OFDM signal; R J (k) represents the covariance matrix of the k-th sub-band of the interference signal; R n (k) represents the covariance matrix of the k-th subband of the noise signal. Since the intensity of the interference signal is much greater than that of the OFDM signal and the noise signal, the above equation is further simplified to...

[0035]

[0036] Among them, R w (k)=R s (k)+R n (k). For the matrix Eigenvalue decomposition yields

[0037]

[0038] in, Represents the sampling covariance matrix Non-zero eigenvalues; U represents The eigenvalues ​​of a given matrix form a unitary matrix consisting of its corresponding eigenvectors, satisfying property UU. H =I. When the interference signal strength is much greater than that of the OFDM signal and the noise signal, these Q eigenvalues ​​are called principal eigenvalues. The eigenvectors corresponding to these Q eigenvalues ​​span 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 largest eigenvalues.

[0039] The value of Q is affected by the number of interfering signal sources. Therefore, this invention utilizes the variance information of the second-order eigenvalue statistics to construct a source number estimation decision function. The calculated... Combined with S J =[e1,e2,…,e Q The subspace of the interference signal can be estimated, and thus the orthogonal complement projection matrix of the interference signal can be obtained.

[0040]

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

[0042]

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

[0044]

[0045] The above equation shows that strong interference signals 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 the multiple signals, the interference-suppressed sub-band data needs to be recombined into M frequency domain signals. IFFT is performed on each of the M signals to convert the frequency domain signals into time domain signals. Two new signal vectors are defined.

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

[0048]

[0049] Where, N sym N represents the number of sampling points for a complete OFDM symbol. CP N represents the number of sampling points for the OFDM symbol cyclic prefix. u N represents the number of sampling points for OFDM symbols excluding the cyclic prefix, and N sym =N u +N CP .z m [(j-1)N sym +i] represents the i-th sampled value of the j-th OFDM symbol in the m-th channel; z(j,i) represents the signal vector composed of the i-th sampled values ​​of the j-th OFDM symbols in all M channels; z(j,i+N u ) represents the (i+N)th OFDM symbol of the j-th channel in all M channels. u The signal vector consists of 10 sampling points.

[0050] Furthermore, in step 2, the synchronization sequence is used to perform time-frequency synchronization on each channel, synchronizing to the positions of the first path and the second path respectively, resulting in two sets of sequences with different synchronization positions.

[0051] Furthermore, in step 3, the two sets of time-frequency synchronized sequences are used to form a multibeamformation using the cyclic characteristics of CP, and the first and second paths are extracted respectively. Taking the first path as an example, an optimization objective function is established.

[0052]

[0053] stω H ω=1

[0054] in, i = 1, 2, ..., N g ,j=1,…,N,Further definition

[0055]

[0056] The optimization function can then be further expressed as:

[0057]

[0058] Constructing the performance function using the Lagrange multiplier method

[0059] L(ω)=ω H Rω-μ(ωH ω-1) (20)

[0060] For ω * Differentiating and setting it to zero, we get

[0061]

[0062] Rω=μω

[0063] From the above equation, we can see that ω is the right eigenvector corresponding to the eigenvalues ​​of matrix R, which are μ. Also, ω H ω=1, therefore

[0064] ω H Rω=μ, (22)

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

[0066]

[0067] Here, z1 represents the sequence synchronized to the head of the first path in step 2. Similarly, 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. OFDM demodulation is performed on the two signals respectively. Then, channel estimation is performed on the two signals using the pilot symbols in the frame structure, and the channel gains H1 and H2 are obtained by interpolation.

[0068] Furthermore, in step 4, the two signals are divided into sub-bands 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] Where, ω m (k,l) represents the weighting factor of the diversity branch of the k-th subband. Finally, the merged output signal is input into the remaining communication demodulation module for further processing.

[0071] Beneficial effects

[0072] The beneficial effects of this invention are as follows: A multi-beam array interference suppression method combining time-frequency analysis and symbol characteristics transforms the time-domain signal to the time-frequency domain and uses an orthogonal projection algorithm to suppress strong interference, effectively suppressing interference signals passing through multipath channels. It extracts the two strongest paths in the OFDM signal using the cyclic characteristics of the CP (Cyclic Point Component) and then combines them using maximum signal-to-noise ratio (SNR) combining. This method improves the diversity gain at the receiver, effectively increasing the SNR and enhancing the reliability of the receiver. Attached Figure Description

[0073] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration:

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

[0075] Figure 2 This is a schematic diagram illustrating the use of CP to obtain the first path according to the present invention;

[0076] Figure 3 This is a schematic diagram illustrating the use of CP to obtain the second path according to the present invention; Detailed Implementation

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

[0078] Consider a Long Term Evolution (LTE) UAV downlink data link scenario with a single broadband suppression interference source. OFDM waveforms are used, where the receiver is configured with M receive antennas, and both the desired signal transmitter and the interference signal transmitter are configured with one transmit antenna. The downlink data signal and the interference signal enter the receiver through different multipath channels.

[0079] like Figure 1 As shown, this invention provides a multi-beam array interference suppression method that combines time-frequency analysis and symbol characteristics, mainly including the following steps:

[0080] Step 1: Since the data link utilizes OFDM wideband 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 into many sub-bands in the frequency dimension for separate analysis. This allows the channel in each sub-band to be approximated as flat fading. With M receiving antennas, the wideband array received signal model for multipath environments after FFT is as follows:

[0081]

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

[0083] Furthermore, the array received signal model is written in matrix operation form as follows:

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

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

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

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

[0088] The specific representation of H1(k,l) is as follows:

[0089]

[0090] The specific representation of H2(k,l) is as follows:

[0091]

[0092] The specific representation of N(k,l) is as follows:

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

[0094] Before the orthogonal projection algorithm begins, the covariance matrix of the k-th sub-band of the received signal needs to be calculated.

[0095] The covariance matrix is ​​defined as

[0096]

[0097] in, This indicates the calculation of the mean. Since the obtained data is discrete sampled data, the sampling covariance matrix is ​​used instead of the covariance matrix. That is, the sampling covariance matrix of the k-th sub-band is expressed as:

[0098]

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

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

[0101]

[0102] Among them, R s (k) represents the covariance matrix of the k-th subband of the OFDM signal; R J (k) represents the covariance matrix of the k-th sub-band of the interference signal; R n (k) represents the covariance matrix of the k-th subband of the noise signal. Since the intensity of the interference signal is much greater than that of the OFDM signal and the noise signal, the above equation is further simplified to...

[0103]

[0104] Among them, R w (k)=R s (k)+R n (k). For the matrix Eigenvalue decomposition yields

[0105]

[0106] in, Represents the sampling covariance matrix Non-zero eigenvalues; U represents The eigenvalues ​​of a given matrix form a unitary matrix consisting of its corresponding eigenvectors, satisfying property UU. H =I. When the interference signal strength is much greater than that of the OFDM signal and the noise signal, these Q eigenvalues ​​are called principal eigenvalues. The eigenvectors corresponding to these Q eigenvalues ​​span 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 largest eigenvalues.

[0107] The value of Q is affected by the number of interfering signal sources. This invention utilizes the variance information of the second-order eigenvalue statistics to construct a source number estimation decision function. Under strong interference conditions, the eigenvalues ​​approximately satisfy...

[0108]

[0109] definition If i = 1, ..., M-1, then

[0110]

[0111] Define the variance of the eigenvalues ​​as ε k ,

[0112]

[0113] Where k = 1, 2, ..., M-1. The constructed observation values ​​are...

[0114]

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

[0116]

[0117] Where C1 and C2 are constants. The decision function can then be written as:

[0118]

[0119] Calculated using the above formula And combined with S J =[e1,e2,…,e Q This allows us to estimate the subspace of the interference signal.

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

[0121]

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

[0123]

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

[0125]

[0126] The above equation shows that strong interference signals 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 the multiple signals, the interference-suppressed sub-band data needs to be recombined into M frequency domain signals. IFFT is performed on each of the M signals to convert the frequency domain signals into time domain signals. Two new signal vectors are defined.

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

[0129]

[0130] Where, N sym N represents the number of sampling points for a complete OFDM symbol. CP N represents the number of sampling points for the OFDM symbol cyclic prefix. u N represents the number of sampling points for OFDM symbols excluding the cyclic prefix, and N sym =N u +N CP .z m [(j-1)N sym +i] represents the i-th sampled value of the j-th OFDM symbol in the m-th channel; z(j,i) represents the signal vector composed of the i-th sampled values ​​of the j-th OFDM symbols in all M channels; z(j,i+N u ) represents the (i+N)th OFDM symbol of the j-th channel in all M channels. u The signal vector consists of 10 sampling points.

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

[0132] Step 3: Utilize the cyclic characteristics of CP to form a multibeamform from the two time-frequency synchronized sequences, extracting the first and second paths respectively. Taking the first path as an example... Figure 2 As shown, establish the optimization objective function.

[0133]

[0134] stω H ω=1

[0135] in, i = 1, 2, ..., N CP ,j=1,…,N,Further definition

[0136]

[0137] The optimization function can then be further expressed as:

[0138]

[0139] Constructing the performance function using the Lagrange multiplier method

[0140] L(ω)=ω H Rω-μ(ω H ω-1) (26)

[0141] For ω * Differentiating and setting it to zero, we get

[0142]

[0143] Rω=μω

[0144] From the above equation, we can see that ω is the right eigenvector corresponding to the eigenvalues ​​μ of matrix R. Multiplying both sides by ω... H get

[0145] ω H Rω=μω H ω (28)

[0146] There is also ω H ω=1, therefore

[0147] ω H Rω=μ, (29)

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

[0149]

[0150] Where z1 represents the sequence synchronized to the head of the first path in step 2. Similarly, as... Figure 3 As shown, we can perform the same operation as in step 3 based on the sequence synchronized to the head of the second path in step 2 to obtain the second path. OFDM demodulation is performed on the two signals respectively. Then, channel estimation is performed on the two signals using the pilot symbols in the frame structure, and the channel gains H1 and H2 are obtained by interpolation.

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

[0152]

[0153] Where, ω m (k,l) represents the weighting factor of the diversity branch of the k-th sub-band. Since the interference signal has already been eliminated in the orthogonal projection interference suppression module, It can be represented as

[0154]

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

[0156]

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

[0158]

[0159] Among them, E s For the desired signal power, Let be the noise signal power. According to the Cauchy-Schwarz inequality, we can obtain...

[0160]

[0161] Observing the above formula, we can find that when The time inequality is transformed into an equality, and the combined outputs yield the maximum signal-to-noise ratio. Then, the weights need to be normalized to obtain the final weight values.

[0162]

[0163] The resulting output signal is:

[0164]

[0165] Finally, the merged 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 and are not intended 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 to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A method of multi-beam array interference mitigation combining time-frequency analysis with symbol features, characterized by, The method comprises the following steps: Step 1: sampling and truncating the received signal of each channel respectively, and then performing Fast Fourier Transform (FFT) to convert the time-domain signal to the time-frequency domain, calculating the sample covariance matrix, estimating the interference signal orthogonal complement space projection matrix by using the Second Order Statistic of Eigenvalues (SORTE) algorithm, performing subspace interference suppression on the molecular band, re-distributing the subcarriers of the signal after interference suppression, and then performing Inverse Fast Fourier Transform (IFFT) to convert the signal to the time domain; Step 2: performing time-frequency synchronization operation on the time-domain signals of different channels obtained in step 1 respectively, and synchronizing to the head of the first path and the head of the second path of the expected signal respectively to generate two groups of sequences; Step 3: extracting the signals of the two stronger paths of the expected signal by using the cyclic characteristic of the Cyclic Prefix (CP), performing Orthogonal Frequency Division Multiplexing (OFDM) demodulation on the extracted two stronger path signals to obtain frequency-domain signals, and performing frequency-domain channel estimation on each path signal by using the pilot symbol and obtaining the channel estimation vector by interpolation; Step 4: calculating the weight according to the maximum signal-to-noise ratio criterion and the channel estimation vector obtained in step 3, and combining the two path signals in the frequency domain to obtain the final output signal.

2. The method of claim 1, wherein the method is characterized by: In step 1, since the data link uses OFDM wideband waveform, 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 multiple subbands in the frequency dimension for separate analysis. In this way, the channel on each subband can be approximated as flat fading. In the case of M receiving antennas, the wideband array received signal model in step 1 facing the multipath environment after FFT of the received signal is where m e {1,2,...,M}, x m (k, l) denotes the signal received by the mth antenna on the kth subcarrier of the lth OFDM symbol; denotes the frequency domain channel response of the desired signal to the kth subcarrier of the lth OFDM symbol of the mth receiving antenna; denotes the frequency domain channel response of the interfering signal to the kth subcarrier of the lth OFDM symbol of the mth receiving antenna; s(k, l) denotes the transmitted signal on the kth subcarrier of the lth OFDM symbol; and J(k, l) denotes the received signal on the kth subcarrier of the lth OFDM symbol of the mth receiving antenna. Interfering signals on the carrier; N m (k, l) represents the additive white Gaussian noise on the mth antenna; The array received signal model is written as a matrix operation form x(k,l)=H1(k,l)s(k,l)+H2(k,l)J(k,l)+N(k,l), (2) wherein is an array received signal vector; is a desired signal frequency domain channel vector; is an interference signal frequency domain channel vector; is a noise signal vector, x(k, l) is specifically represented as x(k, l) = [xl(k, l), x2(k, l),..., xN(k, l)]T(1) M (k, l) = [xl(k, l), x2(k, l),..., xN(k, l)]T(1) The specific form of H1(k,l) is The specific form of H2(k,l) is The specific form of N(k,l) is N(k, l) = [N1(k, l), N2(k, l),..., N M (k, l)] (6) Before the orthogonal projection algorithm starts, the covariance matrix of the kth subband of the received signal needs to be calculated, which is defined as wherein denotes the averaging, and since the data obtained are discrete samples, the covariance matrix is replaced by a sample covariance matrix, i.e. the sample covariance matrix of the kth subband is denoted by where L is the number of OFDM symbols required for the calculation, and considering that the OFDM signal, the interference signal, and the noise signal are statistically independent of each other, the sample covariance matrix is expressed as wherein, denotes the covariance matrix of the kth subband of the OFDM signal; denotes the covariance matrix of the kth subband of the interference signal; denotes the covariance matrix of the kth subband of the noise signal, and since the interference signal is much stronger than the OFDM signal and the noise signal, the above equation is further simplified to wherein performing eigenvalue decomposition on the matrix yields wherein, are non-zero eigenvalues of the sample covariance matrix , and U represents a unitary matrix composed of eigenvalues of the sample covariance matrix and corresponding eigenvector columns, and satisfies UU H = I, I is an identity matrix, since the interference signal strength is much greater than the OFDM signal and noise signal, so the K eigenvalues are called main eigenvalues, and the corresponding eigenvectors span the interference signal subspace, denoted as S J = [e1, e2, …, e K ], wherein e i (i = 1, 2, …, K) represents an eigenvector corresponding to the K largest eigenvalues of the sample covariance matrix , and the value of K is affected by the number of interference signal sources. Specifically, an eigenvalue second-order statistical variance information is used to construct a source number estimation decision function, and in the case of strong interference, the eigenvalues approximately satisfy The eigenvalues are used to estimate the interference orthogonal complement space projection matrix by SORTE algorithm. First, define then The variance of the defined characteristic value is δ k , Where k=1,2,…,M-1, and the observation value is constructed as According to the above formula, the observation value satisfies the following relationship Where c1 and c2 are constants, and the decision function is written as Calculated using the above formula And combined with S J =[e1,e2,…,e K The subspace of the interference signal can be estimated, and using this subspace, the orthogonal complement projection matrix of the interference signal can be calculated. Where I is an MxM identity matrix, and the received signal vector x(k,l) is projected to the orthogonal complement space of the interference signal to obtain where s(k, l) = H1(k, l)s(k, l), J(k, l) = H2(k, l)J(k, l), taking into account The above equation can be simplified as The above formula shows that the strong interference signal can be eliminated by projecting the received signal vector to the orthogonal complement space of the interference signal, since the orthogonal projection interference suppression does not combine the multiple signals, the sub-band data after interference suppression needs to be recombined into M frequency domain signals, IFFT is performed on the M signals respectively, the frequency domain signals are converted into time domain signals, and two new signal vectors are defined z(j,i) = [z1[(j1)N s +i],…,z M [(j1)N s +i]] T (21) z(j, i+N u ) = [z1[(j1)N s +i+N u ],...,z M [(j1)N s +i+N u ]] T (22) wherein N s represents the number of sampling points of a complete OFDM symbol, N g represents the number of sampling points of the cyclic prefix of an OFDM symbol, N u represents the number of sampling points of an OFDM symbol excluding the cyclic prefix, and N s =N u N g ,z m [(j1)·N s +i] represents the i-th sampling value of the j-th OFDM symbol in the m-th channel, m is the serial number of the frequency domain signal after recombination of the subband data after interference suppression; z(j,i) represents a signal vector composed of the i-th sampling value of the j-th OFDM symbol of all M channels; and z(j,i+N u ) represents a signal vector composed of the i+N u -th sampling point of the j-th OFDM symbol of all M channels.

3. The method of claim 1, wherein: In step 2, time-frequency synchronization is performed on each channel by using a synchronization sequence, and each channel is synchronized to the positions of the first path and the second path respectively, thereby obtaining two groups of sequences with different synchronization positions.

4. The method of claim 1, wherein: In step 3, the cyclic characteristics of the CP are used to extract the first path and the second path from the two groups of sequences after time-frequency synchronization, for the first path, a target function is established according to the maximum correlation wherein i = 1, 2,..., N g , j = 1,..., N, z(j, i+N u ) represents a signal vector of the (i+N u )th sample point of the jth OFDM symbol of all M channels, N represents the number of OFDM symbols, z(j, i) represents a signal vector of the ith sample value of the jth OFDM symbol of all M channels, N g represents the number of sample points of the OFDM symbol cyclic prefix, N u represents the number of sample points of the OFDM symbol excluding the cyclic prefix, and N s = N u N g , further defined The optimization function is further expressed as A performance function is constructed by using the Lagrange multiplier method For ω * Taking derivative and setting it to zero gives From the above equation, ω is the eigenvalue of the matrix with the right eigenvector corresponding to μ, and also ω H ω = 1, so that The original objective function is to make maximum, so the optimal weight ω is the right eigenvector corresponding to the largest eigenvalue of the matrix The first radial is extracted by weighting the sequence of the first radial to which the synchronization is achieved that is Wherein, z1 represents the sequence of synchronizing to the first diameter head, and the sequence of synchronizing to the second diameter head can be obtained by the same operation The two extracted path signals are respectively subjected to OFDM demodulation, the output signals of the OFDM demodulation module are subjected to frequency domain channel estimation by using pilot symbols, and channel gains H1 and H2 are obtained by interpolation 2。 5. The method of claim 1, wherein: In step 4, the two signals are combined by using maximum signal-to-noise ratio combination in the frequency domain. Specifically, the signal of branch m is r m The output signal (k, l) is written as where ω m (k, l) represents the weighting factor of the kth subband of the diversity branch, since the interference signal has been eliminated in the orthogonal projection interference suppression module, and is expressed as where H m (k, l) is the channel frequency domain response of the desired signal, s(k, l) represents the transmitted signal on the kth subcarrier of the lth OFDM symbol, N m (k, l) represents the additive white Gaussian noise on the mth antenna, and the above two equations are combined The signal-to-noise ratio of the received signal is written as where E s is the desired signal power, is the noise signal power, according to the Cauchy-Schwarz inequality It can be found from the above equation that when , is the transpose of , the inequality becomes equality, the combined output gets the maximum SNR, and then the weights need to be normalized to get the final weights The output signal obtained is Finally, the combined output signal is further processed.

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