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A robust array beamforming method based on compressed covariance matrix sensing

A covariance matrix and beam technology, which is applied in the field of robust array beamforming, can solve the problems of low main-side lobe ratio of beam spatial spectrum and low SINR value of adaptive beam output.

Active Publication Date: 2020-07-07
HARBIN INST OF TECH
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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem that the array error is modeled as the SIRV model and the existing beamforming algorithm beam space spectrum main-sidelobe ratio is not high under the condition of non-ideal compressed sensing, and the adaptive beamforming output SINR value is low, and proposed A robust array beamforming method based on compressed covariance matrix sensing

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  • A robust array beamforming method based on compressed covariance matrix sensing
  • A robust array beamforming method based on compressed covariance matrix sensing
  • A robust array beamforming method based on compressed covariance matrix sensing

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specific Embodiment approach 1

[0017] Embodiment 1: A method of robust array beamforming based on compressed covariance matrix sensing includes the following steps:

[0018] signal model

[0019] The estimation of the covariance matrix of coherent signals and interference received by a uniform linear array with N array elements is discussed. 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 The interference direction is time-varying, which can be expressed as:

[0020]

[0021] in Indicates the center position of the interference direction, Represents the absolute amount of the range of change, rand(t) represents a random number that changes uniformly between [-1,1]. In the case that there is no error in the steering vec...

specific Embodiment approach 2

[0111] Specific embodiment 2: the difference between this embodiment and specific embodiment 1 is that the signal covariance matrix R is constructed in the step 2 x The specific expression of is:

[0112]

[0113] where R i Represents the estimated i-th covariance matrix, i=0,...,I-1, I represents the total number of estimated covariance matrices, Represents the weight of the linear combination.

[0114] Use formula (9) and the method in the literature (Ahmed O. Nasif, Zhi Tian, ​​Qing Ling. High-dimensionalSparse Covariance Estimation for Random Signals. Daniel Romero, GeertLeus. Compressive Covariance Sampling) to construct the sampling covariance matrix.

[0115] Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0116] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: the number of beam subspaces and the beam width in the beam space method described in step two are specifically:

[0117]

[0118]

[0119] where R number is the beam subspace number, R null is the beam width, p max is the main lobe value of the beam space spectrum, p submax is the maximum sidelobe value of the beam space spectrum.

[0120] Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

A robust array beamforming method based on compressed covariance matrix sensing, the invention relates to a robust array beamforming method based on compressed covariance matrix sensing. The invention aims to solve the problems that the array error is modeled as a SIRV model and the existing beamforming algorithm beam space spectrum main-side lobe ratio is not high and the adaptive beamforming output SINR value is low under the condition of non-ideal compressed sensing. The present invention comprises: one: structure signal covariance matrix R x Using the expression of the sampling covariance matrix, signal steering vector matrix and interference steering vector matrix, and using the beam space method to solve the signal covariance matrix R x ; Two: optimize and solve the robust adaptive beamforming algorithm model, and obtain the beamformer weight w; three: the signal covariance matrix R x The beamformer weight w is used as the initial value for iterative optimization until convergence, and the final beamformer weight w is obtained opt . The invention is used in the technical field of smart antennas.

Description

technical field [0001] The invention relates to the technical field of smart antennas, in particular to a robust array beamforming method. Background technique [0002] For decades, adaptive beamforming technology has attracted the attention of many researchers, and this technology is widely used in radar, communication, navigation, aerospace and biomedicine. According to different calculation methods, adaptive beamforming techniques can be roughly divided into two categories. One is the algorithm based on the reference signal, such as the LMS algorithm (Widrow B, Mantey P E, GriffithsL J, and Goode B B. Adaptive antenna systems. Proc. IEEE, 1967, 55:2143-2159. GitlinR D, Weinstein S D. On the design of gradient algorithms for digitally implemented adaptive filters.IEEE Trans on CT,1973,2:125-136.[3]Nagumo J I,and Noda A.A learning method for system identification.IEEE Trans.Autom.Control,1967,12:282 -287) and DMI algorithm (Albert A E, and Gardner LS. Stochastic Approxima...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/00G06F18/2113G06F18/2411
Inventor 侯煜冠黄清鸿孙晓宇高荷福
Owner HARBIN INST OF TECH