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Adaptive Beamforming Method Based on Maximum Likelihood Resampling

An adaptive beam, maximum likelihood technology, applied in diversity/multi-antenna systems, space transmit diversity, electrical components, etc., can solve the problem of inability to remove the influence of the interference suppression effect, and achieve the improvement and reduction of the interference suppression effect. Influence, the effect of high signal-to-interference-noise ratio

Active Publication Date: 2021-09-28
HARBIN INST OF TECH
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Problems solved by technology

[0003] The purpose of the present invention is to solve the existing process of suppressing the influence of interference signals, in which the desired signal will have an impact on the processing process, but the processing process cannot remove the influence of the information of the desired signal on the effect of suppressing interference, and proposes a method based on Adaptive Beamforming Method for Maximum Likelihood Resampling

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  • Adaptive Beamforming Method Based on Maximum Likelihood Resampling
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  • Adaptive Beamforming Method Based on Maximum Likelihood Resampling

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specific Embodiment approach 1

[0017] The adaptive beamforming method based on maximum likelihood resampling in this embodiment, the adaptive beamforming technology can form narrow beams in the desired signal direction and form nulls in the interference direction, and has been widely used in radar, sonar, wireless communication and other fields.

[0018] Ideally, it is assumed that a uniform linear array with N array elements is used to receive coherent signals and coherent interference, and the array element spacing is d; at time t, there are K narrowband coherent signals and P narrowband coherent interferences, denoted as s 1 (t),s 2 (t),...,s K (t) and J 1 (t),J 2 (t),...,J P (t), their wavelengths are λ; the incoming wave direction of the target signal is θ 1 ,θ 2 ,…,θ K , the interference direction is where the interference direction is time-varying and can be expressed as

[0019]

[0020] In the formula, Indicates the central position of p(p=1,2,…P) interference angles, Represents th...

specific Embodiment approach 2

[0044] The difference from the specific embodiment 1 is that in the adaptive beamforming method based on maximum likelihood resampling in this embodiment, the M snapshot sampling data of the received signal are used as samples in step 1, and the covariance of the samples is calculated The matrix process is, in practice, the interference plus noise covariance matrix R p+ξ It cannot be directly obtained, so the M snapshot sampling data of the received signal are used as samples for calculation, and the sample covariance matrix of the received signal is obtained as:

[0045]

[0046] and use instead of matrix R p+ξ Re-solve to get the optimal weight vector:

[0047]

specific Embodiment approach 3

[0048]The difference from the specific embodiment 1 or 2 is that in the adaptive beamforming method based on maximum likelihood resampling in this embodiment, the process of using the particle filter to process the M covariance matrices described in step 2 is ,

[0049] Ideally the standard Capon beamforming algorithm uses the interference-plus-noise covariance matrix R p+ξ , but in practical applications the received signal sample covariance matrix and contains the desired signal information, so the Capon beamforming algorithm in actual use is more sensitive to the information of the desired signal and is more easily affected by the desired signal.

[0050] Considering the compressed array, as shown in the figure (the incoming wave angle of the signal source is θ), we only keep N' active antennas (such as figure 1 shown by the solid line in the center), and their labels are respectively q 0 ,...,q N'-1 . The data received by the subarray at time t can be regarded as c...

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Abstract

Adaptive Beamforming Method Based on Maximum Likelihood Resampling. In the existing process of suppressing the influence of the interference signal, the influence of the information of the desired signal on the effect of suppressing the interference cannot be removed. The present invention is to calculate the covariance matrix of the M snapshot sampling data of the received signal; use the particle filter and the beam space processing method to process the estimated covariance matrix, obtain the estimated noise plus interference covariance matrix and calculate the corresponding maximum of the matrix Likelihood estimation, select h larger maximum likelihood estimates and corresponding noise-plus-interference covariance matrices; normalize the h estimates to obtain the weight of the noise-plus-interference covariance matrix; obtain the estimated The noise-plus-interference covariance matrix is ​​multiplied by the weight of the noise-plus-interference covariance matrix and summed to obtain the final estimated result and bring it into the calculation formula of the beamforming weight vector to obtain the beamforming weight vector. Compared with the commonly used standard Capon beamforming, the method of the present invention can reduce the influence of desired signal information on the interference suppression effect.

Description

technical field [0001] The invention relates to an adaptive beamforming method based on maximum likelihood resampling. Background technique [0002] Adaptive beamforming technology can form narrow beams in the desired signal direction and form nulls in the interference direction, and has been widely used in radar, sonar, wireless communication and other fields. When estimating the direction of arrival of a signal, there are often interference signals, and the standard Capon beamforming algorithm is a commonly used method for suppressing interference signals. Ideally, the optimal weight vector of the standard Capon beamforming algorithm is Among them, R p+ξ is the interference plus noise covariance matrix, but in practice, R p+ξ It cannot be obtained directly, so N snapshot sampling data of the received signal are generally used as samples, and the sample covariance matrix of the received signal is used instead of R p+ξ To calculate the weight vector, that is, the weig...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04B7/08
CPCH04B7/086
Inventor 侯煜冠高荷福陈迪孙晓宇毛兴鹏
Owner HARBIN INST OF TECH
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