Self-interference suppression method based on MVDR joint coordinate return-to-zero

Abstract

The invention discloses a self-interference suppression method based on MVDR joint coordinate return-to-zero, and the method comprises the steps: receiving signal data through each array element, and carrying out the estimation of the direction of arrival of a desired signal through a DOA algorithm; processing the signal data by using an MVDR beam forming algorithm based on the desired signal direction of arrival; mapping the residual error signal data after array processing into a Walsh domain, and taking any code element as information for processing; the method comprises the following steps: performing amplitude truncation on a signal in a Walsh domain, suppressing a relatively strong self-interference component, and returning to a time domain through inverse mapping, judging whether to perform amplitude truncation operation by utilizing a relationship between a signal after Walsh domain transformation and a defined threshold value, when the signal in the Walsh domain is greater than or equal to the defined threshold value, truncating the strong self-interference component in the signal in the Walsh domain, and performing zero setting, so that the self-interference component in the signal in the Walsh domain is suppressed. According to the method, efficient target signal extraction and interference suppression under the condition that the target direction is unknown are realized, and the overall suppression depth is improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular to a self-interference suppression method based on MVDR combined with coordinate zeroing. Background Art

[0002] Traditional self-interference suppression methods mainly focus on the following four aspects: First, passive suppression methods in the propagation domain usually rely on setting acoustic barriers or isolation structures to reduce transceiver coupling. However, such methods are highly dependent on the actual platform structure and have limited suppression effects in scenarios with strong self-interference at close range. Second, self-interference cancellation methods in the analog domain adjust the amplitude and phase of the transmitted signal through a filter structure and then subtract it, which have advantages such as good real-time performance and front-end linear protection. However, such methods have extremely high requirements for signal synchronization and amplitude matching accuracy, with high actual implementation complexity and difficult system debugging.

[0003] At the same time, existing adaptive filtering methods in the digital domain (such as LMS, RLS, etc.) perform online learning and cancellation by constructing an interference channel model and have strong suppression capabilities under certain conditions. However, this method highly depends on the quality of the reference signal and often requires obtaining a high-fidelity transmitted signal from the output end of the power amplifier to achieve better effects, which limits its application in low-power systems or scenarios with limited reference paths. Finally, traditional algorithms in the spatial domain such as conventional beamforming and multi-channel correlation cancellation mainly rely on the array structure to independently achieve interference suppression, lacking coordination with other processing domains, and the overall suppression performance is limited. Summary of the Invention

[0004] In view of the above-mentioned prior art, the present invention aims to provide a self-interference suppression method based on MVDR combined with coordinate zeroing, mainly solving the technical problems existing in the above background art.

[0005] To achieve the above object, the technical solution of the embodiment of the present invention is implemented as follows: A self-interference suppression method based on MVDR combined with coordinate zeroing includes the following steps: Step 1: Each array element receives signal data, and uses the DOA algorithm to estimate the direction of arrival of the desired signal, where the desired signal includes a far-end desired signal; Preferably, the far-end desired signal is , the near-end interference signal is , the direction of arrival angles are and , and the output sound pressure when the two signals are independent is calculated through the far-end desired signal and the near-end interference signal . The calculation formula is:

[0006] Where At the output sound pressure of the nth array element, is the array response coefficient of the array element to the direction signal, is the array response coefficient of the array element to

[0007] Step 2: Process the signal data using the MVDR beamforming algorithm based on the arrival direction of the desired signal; Preferably, for the far - end desired signal and the near - end interference signal both use spread - spectrum modulation, and the spread - spectrum modulation is to perform time - domain multiplication of the modulation symbol and the spread - spectrum chip pulse.

[0008] Preferably, processing the signal data using the MVDR beamforming algorithm based on the arrival direction of the desired signal includes: constructing a signal model, defining a direction vector and a direction matrix, and outputting a signal matrix; when the beam output power reaches the minimum, the MVDR algorithm ensures that the signal output power in the desired direction remains unchanged, obtains the optimal weight of the MVDR by constructing a cost function, when the interference power is much greater than the ambient noise power, uses the matrix inversion lemma to simplify the calculation of the inverse matrix of the spatio - temporal correlation matrix, and finally outputs the received signal at the final receiver by adding the target signal, the interference signal, and the residual ambient noise.

[0009] Step 3: Map the residual error signal data after array processing into the Walsh domain, and take any one of the code elements as information for processing; Preferably, mapping the residual error signal data after array processing into the Walsh domain and taking any one of the code elements as information for processing specifically includes: constructing a cyclic cross - matrix, mapping the signal after array processing into the Walsh domain, taking a certain code element as information for processing, that is, realizing the mapping of the signal in the Walsh domain through matrix multiplication.

[0010] Step 4: Perform amplitude truncation on the signal in the Walsh domain, suppress the stronger self - interference component, and return to the time domain through inverse mapping.

[0011] Preferably, define a threshold η to perform amplitude truncation operation on the Walsh domain signal to suppress the stronger self - interference component. When the Walsh domain signal is less than the defined threshold, keep the original value. When the Walsh domain signal is greater than or equal to the defined threshold, it is determined as strong self - interference and the strong self - interference component in the Walsh domain signal is truncated and set to zero.

[0012] The beneficial effects of the present invention are as follows: (1) The present invention proposes a blind beamforming method that does not rely on the direction of the incoming signal. By combining the MVDR algorithm with the coordinate zeroing technique, it can efficiently extract target signals and suppress interference without knowing the target direction. (2) The present invention constructs a joint interference suppression mechanism of spatial domain filtering and code domain zeroing. It uses MVDR to spatially filter the main interference direction and combines coordinate zeroing to further suppress the residual interference in the code domain, significantly improving the overall suppression depth. (3) The present invention enhances the robustness of the system under strong interference and low signal-to-noise ratio conditions, and can still stably extract target signals in a channel environment dominated by interference. (4) The overall algorithm structure of the present invention is modular, with low computational complexity, which is convenient for integration and implementation in low-power devices and suitable for deployment on resource-constrained platforms. Description of the Drawings

[0013] Figure 1 It is a schematic flowchart of the self-interference suppression method based on MVDR combined with coordinate zeroing provided by the embodiment of the present application. Figure 2 It is a schematic diagram of the angle of the received signal provided by the embodiment of the present application. Detailed Embodiments The technical solution of the present invention will be further elaborated in detail below in conjunction with the drawings in the specification and specific embodiments. Unless otherwise defined, all technical and scientific terms used in this embodiment have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention in this embodiment are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. In the following description, the expression "some embodiments" describes a subset of all possible embodiments. However, it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0014] In the following description, a large number of specific details are given to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, some well-known technical features are not described to avoid confusion with the present invention.

[0015] It should be understood that the present invention can be implemented in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. The purpose of the terms used herein is only to describe specific embodiments and is not a limitation of the present invention. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the terms "comprising" and / or "including", when used in this specification, specify the presence of the stated features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups. As used herein, the term "and / or" includes any and all combinations of the related listed items.

[0016] It should be further noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "inner", "outer", "left", "right" and similar expressions used in this embodiment are for illustrative purposes only and do not represent the only implementation.

[0017] To fully understand the present invention, detailed structures will be presented in the following description to illustrate the technical solutions proposed by the present invention. The optional embodiments of the present invention are described in detail below. However, in addition to these detailed descriptions, the present invention can also have other implementations.

[0018] Embodiment 1 The embodiment of the present invention refers to the attached Figure 1 to provide a self-interference suppression method based on MVDR combined with coordinate zeroing, including the following steps: Step 1: The array elements receive signal data, and the direction of arrival (DOA) algorithm is used to estimate the direction of arrival of the desired signal, and the desired signal includes the far-end desired signal; In this embodiment, the signal is defined as the far-end desired signal, is defined as the near-end interference signal, and are two different direction-of-arrival angles. Assuming that the direction-of-arrival angle of is 30 degrees, the direction-of-arrival angle of is 90 degrees, and the two signals are independent. Then at time the output sound pressure of the

[0019] Among them, is the array response coefficient of the element to the direction signal, is the array response coefficient of the element to the direction signal, is the interference background noise received by the th element, which is Gaussian white noise.

[0020] Step 2: Process the signal data using the MVDR beamforming algorithm based on the direction of arrival of the desired signal; In this embodiment, both the far - end desired signal and the near - end interference signal adopt spread - spectrum modulation, which can be further expanded into a combination of corresponding modulation symbols and spread - spectrum chip pulses. Therefore, the signal model is further clarified as:

[0021] Among them, the symbol represents the convolution operation, 、 respectively represent the near - end and far - end modulation symbols of the th symbol period. The spread - spectrum code sequences are respectively represented by and . The single - chip width is , where is the duration period of a single symbol, and the time delay of the far - end signal relative to the near - end signal is represented by . is the chip - shaping filter, which is used to limit the signal bandwidth, is the chip period, is the th chip, is the channel impulse response of the near - end signal, is the channel impulse response of the far - end signal, is the additive Gaussian white noise.

[0022] Specifically, the direction vector represents the direction of the incident plane wave incident on each element. The direction vector is defined by the vector symbol as:

[0023] Among them, ) indicates that there are 8 elements in the column. Each element has a different position, and respectively represents the response coefficient of the th element to the direction , Indicates the transpose symbol; Direction matrix Indicates the directions in which two incident plane waves are incident on each array element. The direction matrix Is defined in vector notation as: The direction angles are and The inversion of the direction vectors:

[0024] Under plane-wave conditions, for a uniform linear array, there is:

[0025] Where: Is expressed as a phase factor, Is the phase difference parameter;

[0026] Substitute to get:

[0027] Among them, Is the array element spacing, Is the speed of sound, Is the signal wavelength, Is the signal angular velocity. When the array element spacing is half the signal wavelength, there is:

[0028]

[0029] The weighted output signal of the array is :

[0030] Where Is the weight vector, Is the conjugate transpose of the weight vector:

[0031] X is the signal matrix:

[0032] Among them, Are the received signals of the first and second signal sources; Furthermore, , is the waveform of the th plane wave received by each array element.

[0033] The power corresponding to the weighted output is:

[0034] wherein \(R\) is called the spatio-temporal correlation matrix:

[0035] where \(H\) represents the conjugate transpose, represents the ensemble average.

[0036] In this embodiment, the MVDR algorithm minimizes the beam output power on the premise of ensuring that the signal output power in the desired direction remains unchanged, which is expressed mathematically as:

[0037]

[0038] First, construct the cost function :

[0039] Differentiate the above formula with respect to and set it to 0. Combining the conditions, the optimal weight of MVDR is obtained as:

[0040] wherein, is the inverse matrix of the spatio-temporal correlation matrix, is the direction vector of the conjugate transpose. Substitute the above formula into , and the angular spectrum of the MVDR output is obtained as:

[0041] When the interference power is much greater than the ambient noise power, the matrix inversion lemma can be used to simplify to get:

[0042] wherein, the square of the modulus of the direction vector , is the ambient noise power, represents the received power of the signal, is the identity matrix; the power of the interference signal in the output is obtained as:

[0043] wherein, is the conjugate transpose of the MVDR weight vector. Since the two signals are uncorrelated and the interference power is much greater than the desired signal power and the ambient noise power, it can be obtained that:

[0044] Among them, is the number of array elements. Finally, the signal received by the receiving end can be expressed as:

[0045] Among them, is the residual environmental noise in the signal after being processed by the MVDR algorithm. Let , the above formula can be simplified to:

[0046] Among them, is the interference suppression coefficient. The final received signal at the receiving end can be obtained by adding the target signal, the interference signal, and the residual environmental noise.

[0047] In this embodiment, when the signal direction is known, a more appropriate signal can be changed to perform the first-stage interference suppression through beamforming to achieve a better cancellation effect. For example, through weighted superposition beamforming, Bartlett beamforming, minimum mean square error beamforming, minimum variance plus multiple constraints, etc.

[0048] Step 3: Map the residual error signal data after array processing into the Walsh domain, and take any one of the code elements as information for processing; In this embodiment, to further eliminate the remaining self-interference component and at the same time protect the far-end signal from obvious loss, it is proposed to map the residual error signal into the Walsh domain for processing. Specifically, a cyclic cross matrix is constructed, and the signal after array processing is mapped into the Walsh domain. Arbitrarily take a certain code element in it as information, and perform processing to obtain , that is, the mapping of the signal in the Walsh domain is realized through matrix multiplication to optimize the interference cancellation effect:

[0049] The matrix is formed by cyclic shifting of the spreading code . Its mathematical expression is:

[0050] Step 4: Perform amplitude truncation on the signal in the Walsh domain to suppress the stronger self-interference component and map it back to the time domain through inverse mapping.

[0051] In this embodiment, through the above-mentioned cyclic cross-operation in the Walsh domain, the energy of the proximal residual self-interference signal is effectively concentrated and enhanced, while the energy of the distal signal with a weak correlation with the proximal spreading code remains dispersed and is not significantly enhanced. Utilizing this characteristic, a threshold η can be defined to perform an amplitude truncation operation on the Walsh domain signal to suppress the stronger self-interference component:

[0052] When the Walsh domain signal is less than the defined threshold η, the original value is retained. When the Walsh domain signal is greater than or equal to the defined threshold η, it is determined as strong self-interference and the strong self-interference component in the Walsh domain signal is truncated and set to zero. Based on the maximum value of the cross-correlation function and the conservative estimation of the channel response, it is ensured that the distal signal is not overly weakened.

[0053] Specifically, to avoid the weakening effect of the above threshold processing on the desired distal signal and for the convenience of setting the threshold in the actual system, in this embodiment, it is assumed that the channel impulse response has been normalized and its change is slow. Therefore, the convolution term can be approximately taken as the maximum value, thereby obtaining a simple and conservative threshold estimation. The value of the threshold η needs to satisfy the following constraint conditions:

[0054] In the formula, represents the modulation symbol of the distal signal, represents the cross-correlation function between the proximal and distal spreading code chips and performs a convolution operation. The specific definition of the cross-correlation function is:

[0055] represents the cross-correlation operation between signals. The signal after coordinate zeroing processing can be inversely mapped back to the time domain, which can further suppress the local self-interference signal and can be expressed as: , thereby improving the subsequent demodulation performance of the distal signal.

[0056] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. The protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A self-interference suppression method based on MVDR combined with coordinate zeroing, characterized in that, It includes the following steps: Step 1: Each array element receives signal data, and the direction of arrival (DOA) of the desired signal is estimated using the DOA algorithm. The desired signal includes the far-end desired signal; Step 2: The signal data is processed using the MVDR beamforming algorithm based on the direction of arrival of the desired signal; Step 3: The residual error signal data after array processing is mapped into the Walsh domain, and each symbol therein is selected one by one as information for processing; Step 4: The signal in the Walsh domain is amplitude-truncated to suppress the stronger self-interference components and then mapped back to the time domain through inverse mapping.

2. The self-interference suppression method based on MVDR combined with coordinate zeroing according to claim 1, wherein: Define the far - end desired signal as , the near - end interference signal as , the direction - of - arrival angles as and . Calculate the output sound pressure when the two signals are independent through the far - end desired signal and the near - end interference signal . The calculation formula is as follows: Among them, is the output sound pressure of the n-th array element, is the array response coefficient of the array element to the m-th direction signal, is the interference background noise received by the n-th array element.

3. A self-interference suppression method based on MVDR combined with coordinate zeroing according to claim 2, characterized in that: The desired remote signal and the proximal interference signal Both use spread-spectrum modulation, which multiplies the modulation symbols and the spread-spectrum chip pulses in the time domain.

4. A self-interference suppression method based on MVDR combined with coordinate zeroing according to claim 3, characterized in that: The processing of the signal data using the MVDR beamforming algorithm based on the direction of arrival of the desired signal includes: constructing a signal model, defining a direction vector and a direction matrix, and outputting a signal matrix; The signal model is expressed as: Among them, the symbol represents a convolution operation, 、 respectively represent the proximal and distal modulation symbols in the th symbol period. The spreading code sequences are respectively represented by and . The single-chip width , is the duration period of a single symbol. The time delay of the distal signal relative to the proximal signal is represented by . is the chip shaping filter, is the chip period, is the th chip, is the channel impulse response of the proximal signal, is the channel impulse response of the distal signal, is the additive white Gaussian noise; The defined direction vector and direction matrix are as follows: The direction vector represents the direction in which the incident plane wave impinges on each array element. The direction vector is defined in vector notation as: Among them, ) indicates that there are 8 array elements in the column, and each array element has a different position, respectively representing the th array element's response coefficient to the direction , and represents the transpose symbol; Direction matrix It represents the directions of two incident plane waves incident on each array element. The direction matrix is defined in vector notation as: the direction angles are and the inversion of the direction vectors: Under the condition of plane waves, for a uniform linear array: Wherein: is expressed as a phase factor, is the phase difference parameter; Substitute to obtain: Among them, is the element spacing, is the speed of sound, is the signal wavelength, is the signal angular velocity. When the element spacing is half of the signal wavelength, there is: The output signal matrix is: The weighted output signal of the array is : wherein is the weight vector, is the conjugate transpose of the weight vector: X is the signal matrix: Among them, are the received signals of the first and second signal sources; , is the waveform received by each array element for the th plane wave; Power corresponding to the weighted output is as follows: where R is called the spatio-temporal correlation matrix: where H represents the conjugate transpose, denotes the ensemble average.

5. A self-interference suppression method based on MVDR combined with coordinate zeroing according to claim 4, characterized in that: The processing of the signal data using the MVDR beamforming algorithm based on the direction of arrival of the desired signal further includes: when the beam output power reaches the minimum, the MVDR algorithm ensures that the signal output power in the desired direction remains unchanged, which is mathematically expressed as: By constructing a cost function : Differentiate the above equation with respect to and set it to 0. Combining the conditions, the optimal weight of MVDR is obtained as follows: Among them, is the inverse matrix of the spatio-temporal correlation matrix, is the direction vector of the conjugate transpose. Substituting the above formula into , the angle spectrum of the MVDR output is obtained as follows: When the interference power is much greater than the ambient noise power, the matrix inversion lemma can be used to perform the simplification to obtain: Among them, the square of the modulus of the direction vector , is the environmental noise power, represents the power of the received signal, is the identity matrix; the power of the interference signal in the output is obtained as follows: Among them, is the conjugate transpose of the MVDR weight vector. Since the interference power of the two uncorrelated signals is much greater than the desired signal power and the ambient noise power, we can obtain: Among them, is the number of array elements. Finally, the signal received by the receiving end can be expressed as: Among them, is the environmental noise residue in the signal processed by the MVDR algorithm. Let , the above formula can be simplified to: Among them, is the interference suppression coefficient, and the received signal at the final receiving end is output by adding the target signal, the interference signal, and the residual ambient noise.

6. A self-interference suppression method based on MVDR combined with coordinate zeroing according to claim 5, characterized in that: Mapping the residual error signal data after array processing into the Walsh domain, and arbitrarily selecting any one of the code elements therein as information for processing, specifically including: constructing a cyclic cross matrix , the signal after array processing is mapped into the Walsh domain, and arbitrarily selecting a certain code element in as information, and processing to obtain , which is expressed as: Matrix is structured by spreading codes formed by cyclic shift, and its mathematical expression is: That is, the mapping of the signal in the Walsh domain is realized through matrix multiplication.

7. A self-interference suppression method based on MVDR combined with coordinate zeroing according to claim 6, characterized in that: Through the cyclic cross-operation in the Walsh domain, the energy of the proximal residual self-interference signal is effectively concentrated and enhanced, while the energy of the far-end signal with a weak correlation with the proximal spreading code remains dispersed and is not enhanced.

8. A self-interference suppression method based on MVDR combined with coordinate zeroing according to claim 7, characterized in that: Define a threshold η to perform an amplitude truncation operation on the signal in the Walsh domain to suppress the stronger self-interference components: When the Walsh domain signal is less than the defined threshold η, the original value is retained. When the Walsh domain signal is greater than or equal to the defined threshold η, it is determined as strong self-interference and the strong self-interference component in the Walsh domain signal is truncated and set to zero.

9. A self-interference suppression method based on MVDR combined with coordinate zeroing according to claim 8, characterized in that: The value of the threshold η satisfies the following constraint conditions: wherein, represents the modulation symbol of the remote signal, represents the cross-correlation function between the proximal and distal spreading code chips and performs a convolution operation. The specific definition of the cross-correlation function is: The cross-correlation operation between the representative signals. The signals after coordinate zeroing processing can be mapped back to the time domain through inverse mapping to suppress the local self-interference signals, which can be expressed as: 。

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