US 12,438,624 B1
Direction of arrival estimation method and device based on steering vector matrix reconstruction
Qiang Li, Shenzhen (CN); Zhenhui Wang, Shenzhen (CN); Lei Huang, Shenzhen (CN); Xinzhu Chen, Shenzhen (CN); Yuhang Xiao, Shenzhen (CN); Weize Sun, Shenzhen (CN); Peichang Zhang, Shenzhen (CN); and Xiaopeng Li, Shenzhen (CN)
Assigned to SHENZHEN UNIVERSITY, Shenzhen (CN)
Filed by SHENZHEN UNIVERSITY, Guangdong (CN)
Filed on Jan. 28, 2025, as Appl. No. 19/039,257.
Claims priority of application No. 202410396218.7 (CN), filed on Apr. 3, 2024.
Int. Cl. H04B 17/20 (2015.01); G06F 17/11 (2006.01); G06F 17/16 (2006.01)
CPC H04B 17/252 (2023.05) [G06F 17/11 (2013.01); G06F 17/16 (2013.01)] 7 Claims
OG exemplary drawing
 
1. A direction of arrival estimation method based on steering vector matrix reconstruction, the method comprising:
obtaining an array sampling covariance matrix according to an array received signal; wherein the array received signal is obtained by receiving a target incident signal using an array antenna; setting a target variable, and limiting a feasible domain of the target variable by using a Hankel matrix transformation operator and a column extraction operator, and determining a first constraint condition of the target variable; the Hankel matrix transformation operator is configured to transform a vector into a Hankel matrix; the column extraction operator is configured to extract columns of a matrix;
characterizing an estimation error of an array covariance matrix based on the target variable and the array sampling covariance matrix, and using the characterized estimation error as a second constraint condition of the target variable; the array covariance matrix is composed of the array sampling covariance matrix and the estimation error of the array covariance matrix;
establishing an initial optimization model according to a preset norm based on partial sum of singular values and constraint conditions of the target variable; the constraint conditions comprise the first constraint condition and the second constraint condition;
determining a multivariable optimization model according to the initial optimization model;
solving the multivariable optimization model based on an alternating direction method of multipliers to obtain an optimal result; the optimal result comprises an optimal target variable;
analyzing the optimal result to obtain a direction of arrival of the target incident signal,
wherein the characterizing an estimation error of an array covariance matrix based on the target variable and the array sampling covariance matrix, and using the characterized estimation error as a second constraint condition of the target variable comprises:
characterizing the array covariance matrix according to the target variable;
characterizing the estimation error of the array covariance matrix according to the characterized array covariance matrix and the array sampling covariance matrix; and
calculating norms of the estimation error to obtain the second constraint condition of the target variable,
wherein the establishing an initial optimization model according to a preset norm based on partial sum of singular values and constraint conditions of the target variable comprises:
replacing the first constraint condition by the preset norm based on partial sum of singular values to obtain a first objective function; and
determining an expression of the initial optimization model according to the second constraint condition and the first objective function, and
wherein the expression of the initial optimization model is:

OG Complex Work Unit Math
where

OG Complex Work Unit Math
represents optimizing a variable to minimize an objective function; U represents a first target variable; γ represents a second target variable; i represents an index; L represents a quantity of the incident signals; custom character represents the Hankel matrix transformation operator; Qi represents the column extraction operator; p represents a series of the norms; s.t. represents the constraint condition; (⋅)H represents a conjugate transpose; R represents the array sampling covariance matrix; I represents an identity matrix; ∥⋅∥F2 represents a Frobenius norm; ∈ represents hyperparameters of the optimization model.