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Rapid signal subspace estimation method

A fast signal and subspace technology, applied in space transmit diversity, satellite radio beacon positioning systems, transmission systems, etc., can solve the problems that numerical robustness cannot be guaranteed, reduce computational complexity, etc., to ensure numerical robustness, The effect of reducing computational complexity

Active Publication Date: 2011-10-05
TSINGHUA UNIV
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Problems solved by technology

The forward iteration of the covariance matrix level is consistent with the calculation amount of G.Xu's method, and the forward iteration of the data level, especially the blocking matrix selection based on the correlation subtraction structure, can greatly reduce its computational complexity. However, its numerical Robustness cannot be guaranteed, especially in finite-precision arithmetic

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Embodiment Construction

[0035] The fast subspace estimation method proposed by the present invention is described as follows with reference to the drawings and embodiments.

[0036] S101: Obtain an observation data model of an antenna array element;

[0037] A uniform antenna array (ULA) with M units receives P narrowband far-field signals. At the kth snapshot moment, the M×1-dimensional complex observation data can be expressed as:

[0038] x(k)=[x 1 (k),x 2 (k),...,x M (k)] T , k=1, 2, ..., N

[0039] =A(θ)s(k)+n(k) (1)

[0040] =[a(θ 1 ), a(θ 2 ),…, a(θ P )][s 1 (k), s 2 (k),...,s P (k)] T

[0041] +[n 1 (k), n 2 (k),...,n M (k)] T

[0042] where x M (k) represents the signal data of the Mth antenna element; s(k), A(θ) and n(k) represent the signal vector, steering matrix and noise vector respectively; N is the sampling support length, a(θ i ) means that the direction of arrival is θ i The signal-steering vector of and has the following form:

[0043]

[0044] in d and λ...

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Abstract

The invention relates to the technical field of digital signal processing, and provides a rapid signal subspace estimation method, which comprises the following steps of: obtaining an initial observed signal of a receiving antenna array element according to an observed data model; constructing new observed data, and iterating the new observed data; constructing a conversion matrix according to an iteration result; and obtaining a column vector of the conversion matrix. In the method provided by the invention, a plurality of stages of Wiener filtering forward iteration are performed on a data level to obtain the conversion matrix for a forward iteration process, and the conversion matrix is further computed rapidly by utilizing the unitary properties of a Householder matrix, thereby obtaining a group of standard base vectors of a signal subspace; and the Householder conversion of the plurality of stages for Wiener filtering forward iteration so as not only to further reduce computational complexity, but also to ensure the numerical robustness of an algorithm under finite accuracy.

Description

technical field [0001] The invention relates to the technical field of digital signal processing, in particular to a method for reducing computational complexity of signal subspace estimation and ensuring numerical robustness. Background technique [0002] With the development of large-scale integrated circuits and digital signal processing technology, the problem of signal subspace estimation has become one of the key issues in many fields, such as direction of arrival (DOA) estimation and rank-reduced space-time adaptive processing of phased array radar (STAP), multi-user detection (Multi-user Detection) of mobile communication, etc. The traditional signal subspace estimation method usually performs eigenvalue decomposition (EVD) on the covariance matrix of the observation data, and the eigenvectors corresponding to large eigenvalues ​​are stretched into the signal subspace, and the remaining eigenvectors are stretched into the noise subspace. However, since the computati...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B7/04H04B17/00H04L1/06H04L25/02G01S19/35
Inventor 庄学彬陆明泉崔晓伟冯振明
Owner TSINGHUA UNIV
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